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| Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization | Francis Bach | https://papers.nips.cc/paper/7286-efficient-algorithms-for-non-convex-isotonic-regression-through-submodular-optimization | |
|---|---|---|---|
| Structure-Aware Convolutional Neural Networks | Jianlong Chang | https://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks | |
| Kalman Normalization: Normalizing Internal Representations Across Network Layers | Guangrun Wang | https://papers.nips.cc/paper/7288-kalman-normalization-normalizing-internal-representations-across-network-layers | |
| HOGWILD!-Gibbs can be PanAccurate | Constantinos Daskalakis | https://papers.nips.cc/paper/7289-hogwild-gibbs-can-be-panaccurate | |
| Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language | Seonghyeon Nam | https://papers.nips.cc/paper/7290-text-adaptive-generative-adversarial-networks-manipulating-images-with-natural-language | |
| IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis | Huaibo Huang | https://papers.nips.cc/paper/7291-introvae-introspective-variational-autoencoders-for-photographic-image-synthesis | |
| Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences | Jeremias Knoblauch | https://papers.nips.cc/paper/7292-doubly-robust-bayesian-inference-for-non-stationary-streaming-data-with-beta-divergences | |
| Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning | Tyler Scott | https://papers.nips.cc/paper/7293-adapted-deep-embeddings-a-synthesis-of-methods-for-k-shot-inductive-transfer-learning | |
| Generalized Inverse Optimization through Online Learning | Chaosheng Dong | https://papers.nips.cc/paper/7294-generalized-inverse-optimization-through-online-learning | |
| An Off-policy Policy Gradient Theorem Using Emphatic Weightings | Ehsan Imani | https://papers.nips.cc/paper/7295-an-off-policy-policy-gradient-theorem-using-emphatic-weightings | |
| Supervised autoencoders: Improving generalization performance with unsupervised regularizers | Lei Le | https://papers.nips.cc/paper/7296-supervised-autoencoders-improving-generalization-performance-with-unsupervised-regularizers | |
| Visual Object Networks: Image Generation with Disentangled 3D Representations | Jun-Yan Zhu | https://papers.nips.cc/paper/7297-visual-object-networks-image-generation-with-disentangled-3d-representations | |
| Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units | Yixi Xu | https://papers.nips.cc/paper/7298-understanding-weight-normalized-deep-neural-networks-with-rectified-linear-units | |
| Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems | Mrinmaya Sachan | https://papers.nips.cc/paper/7299-learning-pipelines-with-limited-data-and-domain-knowledge-a-study-in-parsing-physics-problems | |
| Learning long-range spatial dependencies with horizontal gated recurrent units | Drew Linsley | https://papers.nips.cc/paper/7300-learning-long-range-spatial-dependencies-with-horizontal-gated-recurrent-units | |
| Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution | Zhisheng Zhong | https://papers.nips.cc/paper/7301-joint-sub-bands-learning-with-clique-structures-for-wavelet-domain-super-resolution | |
| Fast Similarity Search via Optimal Sparse Lifting | Wenye Li | https://papers.nips.cc/paper/7302-fast-similarity-search-via-optimal-sparse-lifting | |
| Learning Deep Disentangled Embeddings With the F-Statistic Loss | Karl Ridgeway | https://papers.nips.cc/paper/7303-learning-deep-disentangled-embeddings-with-the-f-statistic-loss | |
| Geometrically Coupled Monte Carlo Sampling | Mark Rowland | https://papers.nips.cc/paper/7304-geometrically-coupled-monte-carlo-sampling | |
| Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation | Siyuan Huang | https://papers.nips.cc/paper/7305-cooperative-holistic-scene-understanding-unifying-3d-object-layout-and-camera-pose-estimation | |
| An Efficient Pruning Algorithm for Robust Isotonic Regression | Cong Han Lim | https://papers.nips.cc/paper/7306-an-efficient-pruning-algorithm-for-robust-isotonic-regression | |
| PAC-learning in the presence of adversaries | Daniel Cullina | https://papers.nips.cc/paper/7307-pac-learning-in-the-presence-of-adversaries | |
| Sparse DNNs with Improved Adversarial Robustness | Yiwen Guo | https://papers.nips.cc/paper/7308-sparse-dnns-with-improved-adversarial-robustness | |
| Snap ML: A Hierarchical Framework for Machine Learning | Celestine Dünner | https://papers.nips.cc/paper/7309-snap-ml-a-hierarchical-framework-for-machine-learning | |
| See and Think: Disentangling Semantic Scene Completion | Shice Liu | https://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion | |
| Chain of Reasoning for Visual Question Answering | Chenfei Wu | https://papers.nips.cc/paper/7311-chain-of-reasoning-for-visual-question-answering | |
| Sigsoftmax: Reanalysis of the Softmax Bottleneck | Sekitoshi Kanai | https://papers.nips.cc/paper/7312-sigsoftmax-reanalysis-of-the-softmax-bottleneck | |
| Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation | Wenqi Ren | https://papers.nips.cc/paper/7313-deep-non-blind-deconvolution-via-generalized-low-rank-approximation | |
| Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC | Tolga Birdal | https://papers.nips.cc/paper/7314-bayesian-pose-graph-optimization-via-bingham-distributions-and-tempered-geodesic-mcmc | |
| MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang | https://papers.nips.cc/paper/7315-metaanchor-learning-to-detect-objects-with-customized-anchors | |
| Image Inpainting via Generative Multi-column Convolutional Neural Networks | Yi Wang | https://papers.nips.cc/paper/7316-image-inpainting-via-generative-multi-column-convolutional-neural-networks | |
| On Misinformation Containment in Online Social Networks | Amo Tong | https://papers.nips.cc/paper/7317-on-misinformation-containment-in-online-social-networks | |
| A^2-Nets: Double Attention Networks | Yunpeng Chen | https://papers.nips.cc/paper/7318-a2-nets-double-attention-networks | |
| Self-Supervised Generation of Spatial Audio for 360° Video | Pedro Morgado | https://papers.nips.cc/paper/7319-self-supervised-generation-of-spatial-audio-for-360-video | |
| How Many Samples are Needed to Estimate a Convolutional Neural Network? | Simon S. Du | https://papers.nips.cc/paper/7320-how-many-samples-are-needed-to-estimate-a-convolutional-neural-network | |
| Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced | Simon S. Du | https://papers.nips.cc/paper/7321-algorithmic-regularization-in-learning-deep-homogeneous-models-layers-are-automatically-balanced | |
| Optimization for Approximate Submodularity | Yaron Singer | https://papers.nips.cc/paper/7322-optimization-for-approximate-submodularity | |
| (Probably) Concave Graph Matching | Haggai Maron | https://papers.nips.cc/paper/7323-probably-concave-graph-matching | |
| Deep Defense: Training DNNs with Improved Adversarial Robustness | Ziang Yan | https://papers.nips.cc/paper/7324-deep-defense-training-dnns-with-improved-adversarial-robustness | |
| Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes | Junqi Tang | https://papers.nips.cc/paper/7325-rest-katyusha-exploiting-the-solutions-structure-via-scheduled-restart-schemes | |
| Implicit Reparameterization Gradients | Mikhail Figurnov | https://papers.nips.cc/paper/7326-implicit-reparameterization-gradients | |
| Training DNNs with Hybrid Block Floating Point | Mario Drumond | https://papers.nips.cc/paper/7327-training-dnns-with-hybrid-block-floating-point | |
| A Model for Learned Bloom Filters and Optimizing by Sandwiching | Michael Mitzenmacher | https://papers.nips.cc/paper/7328-a-model-for-learned-bloom-filters-and-optimizing-by-sandwiching | |
| Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis | Haoye Dong | https://papers.nips.cc/paper/7329-soft-gated-warping-gan-for-pose-guided-person-image-synthesis | |
| Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions | Minhyuk Sung | https://papers.nips.cc/paper/7330-deep-functional-dictionaries-learning-consistent-semantic-structures-on-3d-models-from-functions | |
| Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling | Yunzhe Tao | https://papers.nips.cc/paper/7331-nonlocal-neural-networks-nonlocal-diffusion-and-nonlocal-modeling | |
| Are ResNets Provably Better than Linear Predictors? | Ohad Shamir | https://papers.nips.cc/paper/7332-are-resnets-provably-better-than-linear-predictors | |
| Learning to Decompose and Disentangle Representations for Video Prediction | Jun-Ting Hsieh | https://papers.nips.cc/paper/7333-learning-to-decompose-and-disentangle-representations-for-video-prediction | |
| Multi-Task Learning as Multi-Objective Optimization | Ozan Sener | https://papers.nips.cc/paper/7334-multi-task-learning-as-multi-objective-optimization | |
| Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search | Zhuwen Li | https://papers.nips.cc/paper/7335-combinatorial-optimization-with-graph-convolutional-networks-and-guided-tree-search | |
| Self-Erasing Network for Integral Object Attention | Qibin Hou | https://papers.nips.cc/paper/7336-self-erasing-network-for-integral-object-attention | |
| LinkNet: Relational Embedding for Scene Graph | Sanghyun Woo | https://papers.nips.cc/paper/7337-linknet-relational-embedding-for-scene-graph | |
| How to Start Training: The Effect of Initialization and Architecture | Boris Hanin | https://papers.nips.cc/paper/7338-how-to-start-training-the-effect-of-initialization-and-architecture | |
| Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? | Boris Hanin | https://papers.nips.cc/paper/7339-which-neural-net-architectures-give-rise-to-exploding-and-vanishing-gradients | |
| Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives | Amit Dhurandhar | https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives | |
| HitNet: Hybrid Ternary Recurrent Neural Network | Peiqi Wang | https://papers.nips.cc/paper/7341-hitnet-hybrid-ternary-recurrent-neural-network | |
| A Unified Framework for Extensive-Form Game Abstraction with Bounds | Christian Kroer | https://papers.nips.cc/paper/7342-a-unified-framework-for-extensive-form-game-abstraction-with-bounds | |
| Removing the Feature Correlation Effect of Multiplicative Noise | Zijun Zhang | https://papers.nips.cc/paper/7343-removing-the-feature-correlation-effect-of-multiplicative-noise | |
| Maximum-Entropy Fine Grained Classification | Abhimanyu Dubey | https://papers.nips.cc/paper/7344-maximum-entropy-fine-grained-classification | |
| On Learning Markov Chains | Yi HAO | https://papers.nips.cc/paper/7345-on-learning-markov-chains | |
| A Neural Compositional Paradigm for Image Captioning | Bo Dai | https://papers.nips.cc/paper/7346-a-neural-compositional-paradigm-for-image-captioning | |
| Quantifying Learning Guarantees for Convex but Inconsistent Surrogates | Kirill Struminsky | https://papers.nips.cc/paper/7347-quantifying-learning-guarantees-for-convex-but-inconsistent-surrogates | |
| Dialog-based Interactive Image Retrieval | Xiaoxiao Guo | https://papers.nips.cc/paper/7348-dialog-based-interactive-image-retrieval | |
| SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator | Cong Fang | https://papers.nips.cc/paper/7349-spider-near-optimal-non-convex-optimization-via-stochastic-path-integrated-differential-estimator | |
| Are GANs Created Equal? A Large-Scale Study | Mario Lucic | https://papers.nips.cc/paper/7350-are-gans-created-equal-a-large-scale-study | |
| Learning Disentangled Joint Continuous and Discrete Representations | Emilien Dupont | https://papers.nips.cc/paper/7351-learning-disentangled-joint-continuous-and-discrete-representations | |
| TADAM: Task dependent adaptive metric for improved few-shot learning | Boris Oreshkin | https://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning | |
| Do Less, Get More: Streaming Submodular Maximization with Subsampling | Moran Feldman | https://papers.nips.cc/paper/7353-do-less-get-more-streaming-submodular-maximization-with-subsampling | |
| Sparse Covariance Modeling in High Dimensions with Gaussian Processes | Rui Li | https://papers.nips.cc/paper/7354-sparse-covariance-modeling-in-high-dimensions-with-gaussian-processes | |
| Deep Neural Nets with Interpolating Function as Output Activation | Bao Wang | https://papers.nips.cc/paper/7355-deep-neural-nets-with-interpolating-function-as-output-activation | |
| FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction | Shuyang Sun | https://papers.nips.cc/paper/7356-fishnet-a-versatile-backbone-for-image-region-and-pixel-level-prediction | |
| Visual Memory for Robust Path Following | Ashish Kumar | https://papers.nips.cc/paper/7357-visual-memory-for-robust-path-following | |
| KDGAN: Knowledge Distillation with Generative Adversarial Networks | Xiaojie Wang | https://papers.nips.cc/paper/7358-kdgan-knowledge-distillation-with-generative-adversarial-networks | |
| Long short-term memory and Learning-to-learn in networks of spiking neurons | Guillaume Bellec | https://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons | |
| Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN | Shupeng Su | https://papers.nips.cc/paper/7360-greedy-hash-towards-fast-optimization-for-accurate-hash-coding-in-cnn | |
| Informative Features for Model Comparison | Wittawat Jitkrittum | https://papers.nips.cc/paper/7361-informative-features-for-model-comparison | |
| PointCNN: Convolution On X-Transformed Points | Yangyan Li | https://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points | |
| Connectionist Temporal Classification with Maximum Entropy Regularization | Hu Liu | https://papers.nips.cc/paper/7363-connectionist-temporal-classification-with-maximum-entropy-regularization | |
| Large Margin Deep Networks for Classification | Gamaleldin Elsayed | https://papers.nips.cc/paper/7364-large-margin-deep-networks-for-classification | |
| Generalizing Graph Matching beyond Quadratic Assignment Model | Tianshu Yu | https://papers.nips.cc/paper/7365-generalizing-graph-matching-beyond-quadratic-assignment-model | |
| Solving Large Sequential Games with the Excessive Gap Technique | Christian Kroer | https://papers.nips.cc/paper/7366-solving-large-sequential-games-with-the-excessive-gap-technique | |
| Discrimination-aware Channel Pruning for Deep Neural Networks | Zhuangwei Zhuang | https://papers.nips.cc/paper/7367-discrimination-aware-channel-pruning-for-deep-neural-networks | |
| On the Dimensionality of Word Embedding | Zi Yin | https://papers.nips.cc/paper/7368-on-the-dimensionality-of-word-embedding | |
| Reinforced Continual Learning | Ju Xu | https://papers.nips.cc/paper/7369-reinforced-continual-learning | |
| Uncertainty-Aware Attention for Reliable Interpretation and Prediction | Jay Heo | https://papers.nips.cc/paper/7370-uncertainty-aware-attention-for-reliable-interpretation-and-prediction | |
| DropMax: Adaptive Variational Softmax | Hae Beom Lee | https://papers.nips.cc/paper/7371-dropmax-adaptive-variational-softmax | |
| Posterior Concentration for Sparse Deep Learning | Veronika Rockova | https://papers.nips.cc/paper/7372-posterior-concentration-for-sparse-deep-learning | |
| A flexible model for training action localization with varying levels of supervision | Guilhem Chéron | https://papers.nips.cc/paper/7373-a-flexible-model-for-training-action-localization-with-varying-levels-of-supervision | |
| A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents | YAN ZHENG | https://papers.nips.cc/paper/7374-a-deep-bayesian-policy-reuse-approach-against-non-stationary-agents | |
| Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited | Di Wang | https://papers.nips.cc/paper/7375-empirical-risk-minimization-in-non-interactive-local-differential-privacy-revisited | |
| Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks | Hang Gao | https://papers.nips.cc/paper/7376-low-shot-learning-via-covariance-preserving-adversarial-augmentation-networks | |
| Learning semantic similarity in a continuous space | Michel Deudon | https://papers.nips.cc/paper/7377-learning-semantic-similarity-in-a-continuous-space | |
| MetaReg: Towards Domain Generalization using Meta-Regularization | Yogesh Balaji | https://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization | |
| Boosted Sparse and Low-Rank Tensor Regression | Lifang He | https://papers.nips.cc/paper/7379-boosted-sparse-and-low-rank-tensor-regression | |
| Domain-Invariant Projection Learning for Zero-Shot Recognition | An Zhao | https://papers.nips.cc/paper/7380-domain-invariant-projection-learning-for-zero-shot-recognition | |
| Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding | Kexin Yi | https://papers.nips.cc/paper/7381-neural-symbolic-vqa-disentangling-reasoning-from-vision-and-language-understanding | |
| Frequency-Domain Dynamic Pruning for Convolutional Neural Networks | Zhenhua Liu | https://papers.nips.cc/paper/7382-frequency-domain-dynamic-pruning-for-convolutional-neural-networks | |
| Quadratic Decomposable Submodular Function Minimization | Pan Li | https://papers.nips.cc/paper/7383-quadratic-decomposable-submodular-function-minimization | |
| A Block Coordinate Ascent Algorithm for Mean-Variance Optimization | Tengyang Xie | https://papers.nips.cc/paper/7384-a-block-coordinate-ascent-algorithm-for-mean-variance-optimization | |
| \ell_1-regression with Heavy-tailed Distributions | Lijun Zhang | https://papers.nips.cc/paper/7385-ell_1-regression-with-heavy-tailed-distributions | |
| Neural Nearest Neighbors Networks | Tobias Plötz | https://papers.nips.cc/paper/7386-neural-nearest-neighbors-networks | |
| Efficient nonmyopic batch active search | Shali Jiang | https://papers.nips.cc/paper/7387-efficient-nonmyopic-batch-active-search | |
| A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers | Omer Ben-Porat | https://papers.nips.cc/paper/7388-a-game-theoretic-approach-to-recommendation-systems-with-strategic-content-providers | |
| Interactive Structure Learning with Structural Query-by-Committee | Christopher Tosh | https://papers.nips.cc/paper/7389-interactive-structure-learning-with-structural-query-by-committee | |
| Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere | Yanjun Li | https://papers.nips.cc/paper/7390-global-geometry-of-multichannel-sparse-blind-deconvolution-on-the-sphere | |
| Video-to-Video Synthesis | Ting-Chun Wang | https://papers.nips.cc/paper/7391-video-to-video-synthesis | |
| How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD | Zeyuan Allen-Zhu | https://papers.nips.cc/paper/7392-how-to-make-the-gradients-small-stochastically-even-faster-convex-and-nonconvex-sgd | |
| Synthesize Policies for Transfer and Adaptation across Tasks and Environments | Hexiang Hu | https://papers.nips.cc/paper/7393-synthesize-policies-for-transfer-and-adaptation-across-tasks-and-environments | |
| Adversarial vulnerability for any classifier | Alhussein Fawzi | https://papers.nips.cc/paper/7394-adversarial-vulnerability-for-any-classifier | |
| Evolution-Guided Policy Gradient in Reinforcement Learning | Shauharda Khadka | https://papers.nips.cc/paper/7395-evolution-guided-policy-gradient-in-reinforcement-learning | |
| Toddler-Inspired Visual Object Learning | Sven Bambach | https://papers.nips.cc/paper/7396-toddler-inspired-visual-object-learning | |
| Alternating optimization of decision trees, with application to learning sparse oblique trees | Miguel A. Carreira-Perpinan | https://papers.nips.cc/paper/7397-alternating-optimization-of-decision-trees-with-application-to-learning-sparse-oblique-trees | |
| FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification | Yixiao Ge | https://papers.nips.cc/paper/7398-fd-gan-pose-guided-feature-distilling-gan-for-robust-person-re-identification | |
| New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity | Pan Zhou | https://papers.nips.cc/paper/7399-new-insight-into-hybrid-stochastic-gradient-descent-beyond-with-replacement-sampling-and-convexity | |
| The Lingering of Gradients: How to Reuse Gradients Over Time | Zeyuan Allen-Zhu | https://papers.nips.cc/paper/7400-the-lingering-of-gradients-how-to-reuse-gradients-over-time | |
| Unsupervised Learning of View-invariant Action Representations | Junnan Li | https://papers.nips.cc/paper/7401-unsupervised-learning-of-view-invariant-action-representations | |
| Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making | Hoda Heidari | https://papers.nips.cc/paper/7402-fairness-behind-a-veil-of-ignorance-a-welfare-analysis-for-automated-decision-making | |
| Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks | Qilong Wang | https://papers.nips.cc/paper/7403-global-gated-mixture-of-second-order-pooling-for-improving-deep-convolutional-neural-networks | |
| Image-to-image translation for cross-domain disentanglement | Abel Gonzalez-Garcia | https://papers.nips.cc/paper/7404-image-to-image-translation-for-cross-domain-disentanglement | |
| Gradient Sparsification for Communication-Efficient Distributed Optimization | Jianqiao Wangni | https://papers.nips.cc/paper/7405-gradient-sparsification-for-communication-efficient-distributed-optimization | |
| Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection | Taylor Mordan | https://papers.nips.cc/paper/7406-revisiting-multi-task-learning-with-rock-a-deep-residual-auxiliary-block-for-visual-detection | |
| Adaptive Online Learning in Dynamic Environments | Lijun Zhang | https://papers.nips.cc/paper/7407-adaptive-online-learning-in-dynamic-environments | |
| FRAGE: Frequency-Agnostic Word Representation | Chengyue Gong | https://papers.nips.cc/paper/7408-frage-frequency-agnostic-word-representation | |
| Generative Neural Machine Translation | Harshil Shah | https://papers.nips.cc/paper/7409-generative-neural-machine-translation | |
| Found Graph Data and Planted Vertex Covers | Austin R. Benson | https://papers.nips.cc/paper/7410-found-graph-data-and-planted-vertex-covers | |
| Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding | Hajin Shim | https://papers.nips.cc/paper/7411-joint-active-feature-acquisition-and-classification-with-variable-size-set-encoding | |
| Regularization Learning Networks: Deep Learning for Tabular Datasets | Ira Shavitt | https://papers.nips.cc/paper/7412-regularization-learning-networks-deep-learning-for-tabular-datasets | |
| Multitask Boosting for Survival Analysis with Competing Risks | Alexis Bellot | https://papers.nips.cc/paper/7413-multitask-boosting-for-survival-analysis-with-competing-risks | |
| Geometry Based Data Generation | Ofir Lindenbaum | https://papers.nips.cc/paper/7414-geometry-based-data-generation | |
| SLAYER: Spike Layer Error Reassignment in Time | Sumit Bam Shrestha | https://papers.nips.cc/paper/7415-slayer-spike-layer-error-reassignment-in-time | |
| On Oracle-Efficient PAC RL with Rich Observations | Christoph Dann | https://papers.nips.cc/paper/7416-on-oracle-efficient-pac-rl-with-rich-observations | |
| Gradient Descent for Spiking Neural Networks | Dongsung Huh | https://papers.nips.cc/paper/7417-gradient-descent-for-spiking-neural-networks | |
| Generalizing Tree Probability Estimation via Bayesian Networks | Cheng Zhang | https://papers.nips.cc/paper/7418-generalizing-tree-probability-estimation-via-bayesian-networks | |
| Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior | Sid Reddy | https://papers.nips.cc/paper/7419-where-do-you-think-youre-going-inferring-beliefs-about-dynamics-from-behavior | |
| Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution | Longquan Dai | https://papers.nips.cc/paper/7420-designing-by-training-acceleration-neural-network-for-fast-high-dimensional-convolution | |
| Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners | Yuxin Chen | https://papers.nips.cc/paper/7421-understanding-the-role-of-adaptivity-in-machine-teaching-the-case-of-version-space-learners | |
| A loss framework for calibrated anomaly detection | https://papers.nips.cc/paper/7422-a-loss-framework-for-calibrated-anomaly-detection | ||
| PacGAN: The power of two samples in generative adversarial networks | Zinan Lin | https://papers.nips.cc/paper/7423-pacgan-the-power-of-two-samples-in-generative-adversarial-networks | |
| Variational Memory Encoder-Decoder | Hung Le | https://papers.nips.cc/paper/7424-variational-memory-encoder-decoder | |
| Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities | Yunwen Lei | https://papers.nips.cc/paper/7425-stochastic-composite-mirror-descent-optimal-bounds-with-high-probabilities | |
| Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation | Yuan Li | https://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation | |
| Overcoming Language Priors in Visual Question Answering with Adversarial Regularization | Sainandan Ramakrishnan | https://papers.nips.cc/paper/7427-overcoming-language-priors-in-visual-question-answering-with-adversarial-regularization | |
| Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang | https://papers.nips.cc/paper/7428-hybrid-knowledge-routed-modules-for-large-scale-object-detection | |
| Bilinear Attention Networks | Jin-Hwa Kim | https://papers.nips.cc/paper/7429-bilinear-attention-networks | |
| Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning | Xing Yan | https://papers.nips.cc/paper/7430-parsimonious-quantile-regression-of-financial-asset-tail-dynamics-via-sequential-learning | |
| Multi-Class Learning: From Theory to Algorithm | Jian Li | https://papers.nips.cc/paper/7431-multi-class-learning-from-theory-to-algorithm | |
| Multivariate Time Series Imputation with Generative Adversarial Networks | Yonghong Luo | https://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | |
| Learning Versatile Filters for Efficient Convolutional Neural Networks | Yunhe Wang | https://papers.nips.cc/paper/7433-learning-versatile-filters-for-efficient-convolutional-neural-networks | |
| Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization | Robert Gower | https://papers.nips.cc/paper/7434-accelerated-stochastic-matrix-inversion-general-theory-and-speeding-up-bfgs-rules-for-faster-second-order-optimization | |
| DifNet: Semantic Segmentation by Diffusion Networks | Peng Jiang | https://papers.nips.cc/paper/7435-difnet-semantic-segmentation-by-diffusion-networks | |
| Conditional Adversarial Domain Adaptation | Mingsheng Long | https://papers.nips.cc/paper/7436-conditional-adversarial-domain-adaptation | |
| Neighbourhood Consensus Networks | Ignacio Rocco | https://papers.nips.cc/paper/7437-neighbourhood-consensus-networks | |
| Relating Leverage Scores and Density using Regularized Christoffel Functions | Edouard Pauwels | https://papers.nips.cc/paper/7438-relating-leverage-scores-and-density-using-regularized-christoffel-functions | |
| Non-Local Recurrent Network for Image Restoration | Ding Liu | https://papers.nips.cc/paper/7439-non-local-recurrent-network-for-image-restoration | |
| Bayesian Semi-supervised Learning with Graph Gaussian Processes | Yin Cheng Ng | https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes | |
| Foreground Clustering for Joint Segmentation and Localization in Videos and Images | Abhishek Sharma | https://papers.nips.cc/paper/7441-foreground-clustering-for-joint-segmentation-and-localization-in-videos-and-images | |
| Video Prediction via Selective Sampling | Jingwei Xu | https://papers.nips.cc/paper/7442-video-prediction-via-selective-sampling | |
| Distilled Wasserstein Learning for Word Embedding and Topic Modeling | Hongteng Xu | https://papers.nips.cc/paper/7443-distilled-wasserstein-learning-for-word-embedding-and-topic-modeling | |
| Learning to Exploit Stability for 3D Scene Parsing | Yilun Du | https://papers.nips.cc/paper/7444-learning-to-exploit-stability-for-3d-scene-parsing | |
| Neural Guided Constraint Logic Programming for Program Synthesis | Lisa Zhang | https://papers.nips.cc/paper/7445-neural-guided-constraint-logic-programming-for-program-synthesis | |
| Genetic-Gated Networks for Deep Reinforcement Learning | Simyung Chang | https://papers.nips.cc/paper/7446-genetic-gated-networks-for-deep-reinforcement-learning | |
| Fighting Boredom in Recommender Systems with Linear Reinforcement Learning | Romain WARLOP | https://papers.nips.cc/paper/7447-fighting-boredom-in-recommender-systems-with-linear-reinforcement-learning | |
| Enhancing the Accuracy and Fairness of Human Decision Making | Isabel Valera | https://papers.nips.cc/paper/7448-enhancing-the-accuracy-and-fairness-of-human-decision-making | |
| Temporal Regularization for Markov Decision Process | Pierre Thodoroff | https://papers.nips.cc/paper/7449-temporal-regularization-for-markov-decision-process | |
| The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning | Jesse Krijthe | https://papers.nips.cc/paper/7450-the-pessimistic-limits-and-possibilities-of-margin-based-losses-in-semi-supervised-learning | |
| Simple random search of static linear policies is competitive for reinforcement learning | Horia Mania | https://papers.nips.cc/paper/7451-simple-random-search-of-static-linear-policies-is-competitive-for-reinforcement-learning | |
| Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization | Yizhe Zhang | https://papers.nips.cc/paper/7452-generating-informative-and-diverse-conversational-responses-via-adversarial-information-maximization | |
| Entropy and mutual information in models of deep neural networks | Marylou Gabrié | https://papers.nips.cc/paper/7453-entropy-and-mutual-information-in-models-of-deep-neural-networks | |
| Collaborative Learning for Deep Neural Networks | Guocong Song | https://papers.nips.cc/paper/7454-collaborative-learning-for-deep-neural-networks | |
| High Dimensional Linear Regression using Lattice Basis Reduction | Ilias Zadik | https://papers.nips.cc/paper/7455-high-dimensional-linear-regression-using-lattice-basis-reduction | |
| Symbolic Graph Reasoning Meets Convolutions | Xiaodan Liang | https://papers.nips.cc/paper/7456-symbolic-graph-reasoning-meets-convolutions | |
| DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors | Arash Vahdat | https://papers.nips.cc/paper/7457-dvae-discrete-variational-autoencoders-with-relaxed-boltzmann-priors | |
| Partially-Supervised Image Captioning | Peter Anderson | https://papers.nips.cc/paper/7458-partially-supervised-image-captioning | |
| 3D-Aware Scene Manipulation via Inverse Graphics | Shunyu Yao | https://papers.nips.cc/paper/7459-3d-aware-scene-manipulation-via-inverse-graphics | |
| Random Feature Stein Discrepancies | Jonathan Huggins | https://papers.nips.cc/paper/7460-random-feature-stein-discrepancies | |
| Distributed Stochastic Optimization via Adaptive SGD | Ashok Cutkosky | https://papers.nips.cc/paper/7461-distributed-stochastic-optimization-via-adaptive-sgd | |
| Precision and Recall for Time Series | Nesime Tatbul | https://papers.nips.cc/paper/7462-precision-and-recall-for-time-series | |
| Deep Attentive Tracking via Reciprocative Learning | Shi Pu | https://papers.nips.cc/paper/7463-deep-attentive-tracking-via-reciprocative-learning | |
| Virtual Class Enhanced Discriminative Embedding Learning | Binghui Chen | https://papers.nips.cc/paper/7464-virtual-class-enhanced-discriminative-embedding-learning | |
| Attention in Convolutional LSTM for Gesture Recognition | Liang Zhang | https://papers.nips.cc/paper/7465-attention-in-convolutional-lstm-for-gesture-recognition | |
| Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang | https://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices | |
| Universal Growth in Production Economies | Simina Branzei | https://papers.nips.cc/paper/7467-universal-growth-in-production-economies | |
| Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors | Fei Jiang | https://papers.nips.cc/paper/7468-bayesian-model-selection-approach-to-boundary-detection-with-non-local-priors | |
| Efficient Stochastic Gradient Hard Thresholding | Pan Zhou | https://papers.nips.cc/paper/7469-efficient-stochastic-gradient-hard-thresholding | |
| SplineNets: Continuous Neural Decision Graphs | Cem Keskin | https://papers.nips.cc/paper/7470-splinenets-continuous-neural-decision-graphs | |
| Generalized Zero-Shot Learning with Deep Calibration Network | Shichen Liu | https://papers.nips.cc/paper/7471-generalized-zero-shot-learning-with-deep-calibration-network | |
| Neural Architecture Search with Bayesian Optimisation and Optimal Transport | Kirthevasan Kandasamy | https://papers.nips.cc/paper/7472-neural-architecture-search-with-bayesian-optimisation-and-optimal-transport | |
| Embedding Logical Queries on Knowledge Graphs | Will Hamilton | https://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs | |
| Learning Optimal Reserve Price against Non-myopic Bidders | Jinyan Liu | https://papers.nips.cc/paper/7474-learning-optimal-reserve-price-against-non-myopic-bidders | |
| Sequential Context Encoding for Duplicate Removal | Lu Qi | https://papers.nips.cc/paper/7475-sequential-context-encoding-for-duplicate-removal | |
| Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning | Supasorn Suwajanakorn | https://papers.nips.cc/paper/7476-discovery-of-latent-3d-keypoints-via-end-to-end-geometric-reasoning | |
| Nonparametric learning from Bayesian models with randomized objective functions | Simon Lyddon | https://papers.nips.cc/paper/7477-nonparametric-learning-from-bayesian-models-with-randomized-objective-functions | |
| SEGA: Variance Reduction via Gradient Sketching | Filip Hanzely | https://papers.nips.cc/paper/7478-sega-variance-reduction-via-gradient-sketching | |
| Automatic Program Synthesis of Long Programs with a Learned Garbage Collector | Amit Zohar | https://papers.nips.cc/paper/7479-automatic-program-synthesis-of-long-programs-with-a-learned-garbage-collector | |
| One-Shot Unsupervised Cross Domain Translation | Sagie Benaim | https://papers.nips.cc/paper/7480-one-shot-unsupervised-cross-domain-translation | |
| Regularizing by the Variance of the Activations' Sample-Variances | Etai Littwin | https://papers.nips.cc/paper/7481-regularizing-by-the-variance-of-the-activations-sample-variances | |
| Overlapping Clustering Models, and One (class) SVM to Bind Them All | Xueyu Mao | https://papers.nips.cc/paper/7482-overlapping-clustering-models-and-one-class-svm-to-bind-them-all | |
| Algorithmic Linearly Constrained Gaussian Processes | Markus Lange-Hegermann | https://papers.nips.cc/paper/7483-algorithmic-linearly-constrained-gaussian-processes | |
| DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning | Runsheng Yu | https://papers.nips.cc/paper/7484-deepexposure-learning-to-expose-photos-with-asynchronously-reinforced-adversarial-learning | |
| Norm matters: efficient and accurate normalization schemes in deep networks | Elad Hoffer | https://papers.nips.cc/paper/7485-norm-matters-efficient-and-accurate-normalization-schemes-in-deep-networks | |
| Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms | Zhihui Zhu | https://papers.nips.cc/paper/7486-dual-principal-component-pursuit-improved-analysis-and-efficient-algorithms | |
| MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval | Helena Peic Tukuljac | https://papers.nips.cc/paper/7487-mulan-a-blind-and-off-grid-method-for-multichannel-echo-retrieval | |
| Mixture Matrix Completion | Daniel Pimentel-Alarcon | https://papers.nips.cc/paper/7488-mixture-matrix-completion | |
| Trajectory Convolution for Action Recognition | Yue Zhao | https://papers.nips.cc/paper/7489-trajectory-convolution-for-action-recognition | |
| The Description Length of Deep Learning models | Léonard Blier | https://papers.nips.cc/paper/7490-the-description-length-of-deep-learning-models | |
| A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem | Sampath Kannan | https://papers.nips.cc/paper/7491-a-smoothed-analysis-of-the-greedy-algorithm-for-the-linear-contextual-bandit-problem | |
| Revisiting Decomposable Submodular Function Minimization with Incidence Relations | Pan Li | https://papers.nips.cc/paper/7492-revisiting-decomposable-submodular-function-minimization-with-incidence-relations | |
| A Practical Algorithm for Distributed Clustering and Outlier Detection | Jiecao Chen | https://papers.nips.cc/paper/7493-a-practical-algorithm-for-distributed-clustering-and-outlier-detection | |
| Learning to Reconstruct Shapes from Unseen Classes | Xiuming Zhang | https://papers.nips.cc/paper/7494-learning-to-reconstruct-shapes-from-unseen-classes | |
| BourGAN: Generative Networks with Metric Embeddings | Chang Xiao | https://papers.nips.cc/paper/7495-bourgan-generative-networks-with-metric-embeddings | |
| Smoothed analysis of the low-rank approach for smooth semidefinite programs | Thomas Pumir | https://papers.nips.cc/paper/7496-smoothed-analysis-of-the-low-rank-approach-for-smooth-semidefinite-programs | |
| Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning | Ofir Marom | https://papers.nips.cc/paper/7497-zero-shot-transfer-with-deictic-object-oriented-representation-in-reinforcement-learning | |
| Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate | Mikhail Belkin | https://papers.nips.cc/paper/7498-overfitting-or-perfect-fitting-risk-bounds-for-classification-and-regression-rules-that-interpolate | |
| Breaking the Span Assumption Yields Fast Finite-Sum Minimization | Robert Hannah | https://papers.nips.cc/paper/7499-breaking-the-span-assumption-yields-fast-finite-sum-minimization | |
| Structured Local Minima in Sparse Blind Deconvolution | Yuqian Zhang | https://papers.nips.cc/paper/7500-structured-local-minima-in-sparse-blind-deconvolution | |
| GIANT: Globally Improved Approximate Newton Method for Distributed Optimization | Shusen Wang | https://papers.nips.cc/paper/7501-giant-globally-improved-approximate-newton-method-for-distributed-optimization | |
| Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data | Xenia Miscouridou | https://papers.nips.cc/paper/7502-modelling-sparsity-heterogeneity-reciprocity-and-community-structure-in-temporal-interaction-data | |
| Non-monotone Submodular Maximization in Exponentially Fewer Iterations | Eric Balkanski | https://papers.nips.cc/paper/7503-non-monotone-submodular-maximization-in-exponentially-fewer-iterations | |
| MetaGAN: An Adversarial Approach to Few-Shot Learning | Ruixiang ZHANG | https://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning | |
| Local Differential Privacy for Evolving Data | Matthew Joseph | https://papers.nips.cc/paper/7505-local-differential-privacy-for-evolving-data | |
| Gaussian Process Conditional Density Estimation | Vincent Dutordoir | https://papers.nips.cc/paper/7506-gaussian-process-conditional-density-estimation | |
| Meta-Gradient Reinforcement Learning | Zhongwen Xu | https://papers.nips.cc/paper/7507-meta-gradient-reinforcement-learning | |
| Modular Networks: Learning to Decompose Neural Computation | Louis Kirsch | https://papers.nips.cc/paper/7508-modular-networks-learning-to-decompose-neural-computation | |
| Learning to Navigate in Cities Without a Map | Piotr Mirowski | https://papers.nips.cc/paper/7509-learning-to-navigate-in-cities-without-a-map | |
| Query Complexity of Bayesian Private Learning | Kuang Xu | https://papers.nips.cc/paper/7510-query-complexity-of-bayesian-private-learning | |
| A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization | Cedric Josz | https://papers.nips.cc/paper/7511-a-theory-on-the-absence-of-spurious-solutions-for-nonconvex-and-nonsmooth-optimization | |
| Recurrent World Models Facilitate Policy Evolution | David Ha | https://papers.nips.cc/paper/7512-recurrent-world-models-facilitate-policy-evolution | |
| Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling | Shannon McCurdy | https://papers.nips.cc/paper/7513-ridge-regression-and-provable-deterministic-ridge-leverage-score-sampling | |
| Wasserstein Variational Inference | Luca Ambrogioni | https://papers.nips.cc/paper/7514-wasserstein-variational-inference | |
| How Does Batch Normalization Help Optimization? | Shibani Santurkar | https://papers.nips.cc/paper/7515-how-does-batch-normalization-help-optimization | |
| Verifiable Reinforcement Learning via Policy Extraction | Osbert Bastani | https://papers.nips.cc/paper/7516-verifiable-reinforcement-learning-via-policy-extraction | |
| Leveraged volume sampling for linear regression | Michal Derezinski | https://papers.nips.cc/paper/7517-leveraged-volume-sampling-for-linear-regression | |
| Model Agnostic Supervised Local Explanations | Gregory Plumb | https://papers.nips.cc/paper/7518-model-agnostic-supervised-local-explanations | |
| A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication | Peng Jiang | https://papers.nips.cc/paper/7519-a-linear-speedup-analysis-of-distributed-deep-learning-with-sparse-and-quantized-communication | |
| Active Learning for Non-Parametric Regression Using Purely Random Trees | Jack Goetz | https://papers.nips.cc/paper/7520-active-learning-for-non-parametric-regression-using-purely-random-trees | |
| Tree-to-tree Neural Networks for Program Translation | Xinyun Chen | https://papers.nips.cc/paper/7521-tree-to-tree-neural-networks-for-program-translation | |
| Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks | Hyeonseob Nam | https://papers.nips.cc/paper/7522-batch-instance-normalization-for-adaptively-style-invariant-neural-networks | |
| Structural Causal Bandits: Where to Intervene? | Sanghack Lee | https://papers.nips.cc/paper/7523-structural-causal-bandits-where-to-intervene | |
| Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog | Sang-Woo Lee | https://papers.nips.cc/paper/7524-answerer-in-questioners-mind-information-theoretic-approach-to-goal-oriented-visual-dialog | |
| A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation | Alexander H. Liu | https://papers.nips.cc/paper/7525-a-unified-feature-disentangler-for-multi-domain-image-translation-and-manipulation | |
| Online Learning with an Unknown Fairness Metric | Stephen Gillen | https://papers.nips.cc/paper/7526-online-learning-with-an-unknown-fairness-metric | |
| Isolating Sources of Disentanglement in Variational Autoencoders | Tian Qi Chen | https://papers.nips.cc/paper/7527-isolating-sources-of-disentanglement-in-variational-autoencoders | |
| Contextual bandits with surrogate losses: Margin bounds and efficient algorithms | Dylan J. Foster | https://papers.nips.cc/paper/7528-contextual-bandits-with-surrogate-losses-margin-bounds-and-efficient-algorithms | |
| Representation Learning for Treatment Effect Estimation from Observational Data | Liuyi Yao | https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data | |
| Representation Balancing MDPs for Off-policy Policy Evaluation | Yao Liu | https://papers.nips.cc/paper/7530-representation-balancing-mdps-for-off-policy-policy-evaluation | |
| Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering | Medhini Narasimhan | https://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering | |
| Causal Discovery from Discrete Data using Hidden Compact Representation | Ruichu Cai | https://papers.nips.cc/paper/7532-causal-discovery-from-discrete-data-using-hidden-compact-representation | |
| Natasha 2: Faster Non-Convex Optimization Than SGD | Zeyuan Allen-Zhu | https://papers.nips.cc/paper/7533-natasha-2-faster-non-convex-optimization-than-sgd | |
| Minimax Statistical Learning with Wasserstein distances | Jaeho Lee | https://papers.nips.cc/paper/7534-minimax-statistical-learning-with-wasserstein-distances | |
| Provable Variational Inference for Constrained Log-Submodular Models | Josip Djolonga | https://papers.nips.cc/paper/7535-provable-variational-inference-for-constrained-log-submodular-models | |
| Learning Hierarchical Semantic Image Manipulation through Structured Representations | Seunghoon Hong | https://papers.nips.cc/paper/7536-learning-hierarchical-semantic-image-manipulation-through-structured-representations | |
| Processing of missing data by neural networks | Marek Śmieja | https://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks | |
| Safe Active Learning for Time-Series Modeling with Gaussian Processes | Christoph Zimmer | https://papers.nips.cc/paper/7538-safe-active-learning-for-time-series-modeling-with-gaussian-processes | |
| Optimal Algorithms for Non-Smooth Distributed Optimization in Networks | Kevin Scaman | https://papers.nips.cc/paper/7539-optimal-algorithms-for-non-smooth-distributed-optimization-in-networks | |
| Computing Higher Order Derivatives of Matrix and Tensor Expressions | Soeren Laue | https://papers.nips.cc/paper/7540-computing-higher-order-derivatives-of-matrix-and-tensor-expressions | |
| Paraphrasing Complex Network: Network Compression via Factor Transfer | Jangho Kim | https://papers.nips.cc/paper/7541-paraphrasing-complex-network-network-compression-via-factor-transfer | |
| Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net | Tom Michoel | https://papers.nips.cc/paper/7542-analytic-solution-and-stationary-phase-approximation-for-the-bayesian-lasso-and-elastic-net | |
| Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation | Chaitanya Ryali | https://papers.nips.cc/paper/7543-demystifying-excessively-volatile-human-learning-a-bayesian-persistent-prior-and-a-neural-approximation | |
| Empirical Risk Minimization Under Fairness Constraints | Michele Donini | https://papers.nips.cc/paper/7544-empirical-risk-minimization-under-fairness-constraints | |
| Unsupervised Learning of Shape and Pose with Differentiable Point Clouds | Eldar Insafutdinov | https://papers.nips.cc/paper/7545-unsupervised-learning-of-shape-and-pose-with-differentiable-point-clouds | |
| Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces | Motoya Ohnishi | https://papers.nips.cc/paper/7546-continuous-time-value-function-approximation-in-reproducing-kernel-hilbert-spaces | |
| Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation | Zhiqiang Xu | https://papers.nips.cc/paper/7547-gradient-descent-meets-shift-and-invert-preconditioning-for-eigenvector-computation | |
| Factored Bandits | Julian Zimmert | https://papers.nips.cc/paper/7548-factored-bandits | |
| Delta-encoder: an effective sample synthesis method for few-shot object recognition | Eli Schwartz | https://papers.nips.cc/paper/7549-delta-encoder-an-effective-sample-synthesis-method-for-few-shot-object-recognition | |
| Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators | Isao Ishikawa | https://papers.nips.cc/paper/7550-metric-on-nonlinear-dynamical-systems-with-perron-frobenius-operators | |
| Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization | Jie Cao | https://papers.nips.cc/paper/7551-learning-a-high-fidelity-pose-invariant-model-for-high-resolution-face-frontalization | |
| Mirrored Langevin Dynamics | Ya-Ping Hsieh | https://papers.nips.cc/paper/7552-mirrored-langevin-dynamics | |
| Moonshine: Distilling with Cheap Convolutions | Elliot J. Crowley | https://papers.nips.cc/paper/7553-moonshine-distilling-with-cheap-convolutions | |
| Stochastic Cubic Regularization for Fast Nonconvex Optimization | Nilesh Tripuraneni | https://papers.nips.cc/paper/7554-stochastic-cubic-regularization-for-fast-nonconvex-optimization | |
| Adaptation to Easy Data in Prediction with Limited Advice | Tobias Thune | https://papers.nips.cc/paper/7555-adaptation-to-easy-data-in-prediction-with-limited-advice | |
| Differentially Private Bayesian Inference for Exponential Families | Garrett Bernstein | https://papers.nips.cc/paper/7556-differentially-private-bayesian-inference-for-exponential-families | |
| Playing hard exploration games by watching YouTube | Yusuf Aytar | https://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube | |
| Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base | Daya Guo | https://papers.nips.cc/paper/7558-dialog-to-action-conversational-question-answering-over-a-large-scale-knowledge-base | |
| Norm-Ranging LSH for Maximum Inner Product Search | Xiao Yan | https://papers.nips.cc/paper/7559-norm-ranging-lsh-for-maximum-inner-product-search | |
| Optimization over Continuous and Multi-dimensional Decisions with Observational Data | Dimitris Bertsimas | https://papers.nips.cc/paper/7560-optimization-over-continuous-and-multi-dimensional-decisions-with-observational-data | |
| Fast Estimation of Causal Interactions using Wold Processes | Flavio Figueiredo | https://papers.nips.cc/paper/7561-fast-estimation-of-causal-interactions-using-wold-processes | |
| When do random forests fail? | Cheng Tang | https://papers.nips.cc/paper/7562-when-do-random-forests-fail | |
| Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes | Ronan Fruit | https://papers.nips.cc/paper/7563-near-optimal-exploration-exploitation-in-non-communicating-markov-decision-processes | |
| Optimistic optimization of a Brownian | Jean-Bastien Grill | https://papers.nips.cc/paper/7564-optimistic-optimization-of-a-brownian | |
| Practical Methods for Graph Two-Sample Testing | Debarghya Ghoshdastidar | https://papers.nips.cc/paper/7565-practical-methods-for-graph-two-sample-testing | |
| NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations | Marco Ciccone | https://papers.nips.cc/paper/7566-nais-net-stable-deep-networks-from-non-autonomous-differential-equations | |
| On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport | Lénaïc Chizat | https://papers.nips.cc/paper/7567-on-the-global-convergence-of-gradient-descent-for-over-parameterized-models-using-optimal-transport | |
| Constructing Deep Neural Networks by Bayesian Network Structure Learning | Raanan Y. Rohekar | https://papers.nips.cc/paper/7568-constructing-deep-neural-networks-by-bayesian-network-structure-learning | |
| Weakly Supervised Dense Event Captioning in Videos | Xuguang Duan | https://papers.nips.cc/paper/7569-weakly-supervised-dense-event-captioning-in-videos | |
| Faithful Inversion of Generative Models for Effective Amortized Inference | Stefan Webb | https://papers.nips.cc/paper/7570-faithful-inversion-of-generative-models-for-effective-amortized-inference | |
| From Stochastic Planning to Marginal MAP | Hao Cui | https://papers.nips.cc/paper/7571-from-stochastic-planning-to-marginal-map | |
| On Binary Classification in Extreme Regions | Hamid JALALZAI | https://papers.nips.cc/paper/7572-on-binary-classification-in-extreme-regions | |
| Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models | Yining Wang | https://papers.nips.cc/paper/7573-near-optimal-policies-for-dynamic-multinomial-logit-assortment-selection-models | |
| Q-learning with Nearest Neighbors | Devavrat Shah | https://papers.nips.cc/paper/7574-q-learning-with-nearest-neighbors | |
| Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization | Pan Xu | https://papers.nips.cc/paper/7575-global-convergence-of-langevin-dynamics-based-algorithms-for-nonconvex-optimization | |
| Asymptotic optimality of adaptive importance sampling | François Portier | https://papers.nips.cc/paper/7576-asymptotic-optimality-of-adaptive-importance-sampling | |
| Learning latent variable structured prediction models with Gaussian perturbations | Kevin Bello | https://papers.nips.cc/paper/7577-learning-latent-variable-structured-prediction-models-with-gaussian-perturbations | |
| The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal | Jiantao Jiao | https://papers.nips.cc/paper/7578-the-nearest-neighbor-information-estimator-is-adaptively-near-minimax-rate-optimal | |
| Deep Reinforcement Learning of Marked Temporal Point Processes | Utkarsh Upadhyay | https://papers.nips.cc/paper/7579-deep-reinforcement-learning-of-marked-temporal-point-processes | |
| Evidential Deep Learning to Quantify Classification Uncertainty | Murat Sensoy | https://papers.nips.cc/paper/7580-evidential-deep-learning-to-quantify-classification-uncertainty | |
| Parsimonious Bayesian deep networks | Mingyuan Zhou | https://papers.nips.cc/paper/7581-parsimonious-bayesian-deep-networks | |
| Single-Agent Policy Tree Search With Guarantees | Laurent Orseau | https://papers.nips.cc/paper/7582-single-agent-policy-tree-search-with-guarantees | |
| Semi-crowdsourced Clustering with Deep Generative Models | Yucen Luo | https://papers.nips.cc/paper/7583-semi-crowdsourced-clustering-with-deep-generative-models | |
| The committee machine: Computational to statistical gaps in learning a two-layers neural network | Benjamin Aubin | https://papers.nips.cc/paper/7584-the-committee-machine-computational-to-statistical-gaps-in-learning-a-two-layers-neural-network | |
| Realistic Evaluation of Deep Semi-Supervised Learning Algorithms | Avital Oliver | https://papers.nips.cc/paper/7585-realistic-evaluation-of-deep-semi-supervised-learning-algorithms | |
| Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward | Lixing Chen | https://papers.nips.cc/paper/7586-contextual-combinatorial-multi-armed-bandits-with-volatile-arms-and-submodular-reward | |
| Training deep learning based denoisers without ground truth data | Shakarim Soltanayev | https://papers.nips.cc/paper/7587-training-deep-learning-based-denoisers-without-ground-truth-data | |
| Re-evaluating evaluation | David Balduzzi | https://papers.nips.cc/paper/7588-re-evaluating-evaluation | |
| Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres | Oisín Moran | https://papers.nips.cc/paper/7589-deep-complex-invertible-networks-for-inversion-of-transmission-effects-in-multimode-optical-fibres | |
| Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals | Tom Dupré la Tour | https://papers.nips.cc/paper/7590-multivariate-convolutional-sparse-coding-for-electromagnetic-brain-signals | |
| Data-Efficient Hierarchical Reinforcement Learning | Ofir Nachum | https://papers.nips.cc/paper/7591-data-efficient-hierarchical-reinforcement-learning | |
| Speaker-Follower Models for Vision-and-Language Navigation | Daniel Fried | https://papers.nips.cc/paper/7592-speaker-follower-models-for-vision-and-language-navigation | |
| Inequity aversion improves cooperation in intertemporal social dilemmas | Edward Hughes | https://papers.nips.cc/paper/7593-inequity-aversion-improves-cooperation-in-intertemporal-social-dilemmas | |
| Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds | David Reeb | https://papers.nips.cc/paper/7594-learning-gaussian-processes-by-minimizing-pac-bayesian-generalization-bounds | |
| Probabilistic Matrix Factorization for Automated Machine Learning | Nicolo Fusi | https://papers.nips.cc/paper/7595-probabilistic-matrix-factorization-for-automated-machine-learning | |
| Stochastic Spectral and Conjugate Descent Methods | Dmitry Kovalev | https://papers.nips.cc/paper/7596-stochastic-spectral-and-conjugate-descent-methods | |
| Recurrent Relational Networks | Rasmus Palm | https://papers.nips.cc/paper/7597-recurrent-relational-networks | |
| But How Does It Work in Theory? Linear SVM with Random Features | Yitong Sun | https://papers.nips.cc/paper/7598-but-how-does-it-work-in-theory-linear-svm-with-random-features | |
| Learning to Optimize Tensor Programs | Tianqi Chen | https://papers.nips.cc/paper/7599-learning-to-optimize-tensor-programs | |
| Boosting Black Box Variational Inference | Francesco Locatello | https://papers.nips.cc/paper/7600-boosting-black-box-variational-inference | |
| Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes | Hassan Ashtiani | https://papers.nips.cc/paper/7601-nearly-tight-sample-complexity-bounds-for-learning-mixtures-of-gaussians-via-sample-compression-schemes | |
| Actor-Critic Policy Optimization in Partially Observable Multiagent Environments | Sriram Srinivasan | https://papers.nips.cc/paper/7602-actor-critic-policy-optimization-in-partially-observable-multiagent-environments | |
| Step Size Matters in Deep Learning | Kamil Nar | https://papers.nips.cc/paper/7603-step-size-matters-in-deep-learning | |
| Derivative Estimation in Random Design | Yu Liu | https://papers.nips.cc/paper/7604-derivative-estimation-in-random-design | |
| Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates | Krishnakumar Balasubramanian | https://papers.nips.cc/paper/7605-zeroth-order-non-convex-stochastic-optimization-via-conditional-gradient-and-gradient-updates | |
| Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments | Daniel Johnson | https://papers.nips.cc/paper/7606-latent-gaussian-activity-propagation-using-smoothness-and-structure-to-separate-and-localize-sounds-in-large-noisy-environments | |
| Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation | JING LI | https://papers.nips.cc/paper/7607-hybrid-mst-a-hybrid-active-sampling-strategy-for-pairwise-preference-aggregation | |
| Infinite-Horizon Gaussian Processes | Arno Solin | https://papers.nips.cc/paper/7608-infinite-horizon-gaussian-processes | |
| Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization | Minshuo Chen | https://papers.nips.cc/paper/7609-dimensionality-reduction-for-stationary-time-series-via-stochastic-nonconvex-optimization | |
| Sequence-to-Segment Networks for Segment Detection | Zijun Wei | https://papers.nips.cc/paper/7610-sequence-to-segment-networks-for-segment-detection | |
| Scaling the Poisson GLM to massive neural datasets through polynomial approximations | David Zoltowski | https://papers.nips.cc/paper/7611-scaling-the-poisson-glm-to-massive-neural-datasets-through-polynomial-approximations | |
| Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games | Yun Kuen Cheung | https://papers.nips.cc/paper/7612-multiplicative-weights-updates-with-constant-step-size-in-graphical-constant-sum-games | |
| Why Is My Classifier Discriminatory? | Irene Chen | https://papers.nips.cc/paper/7613-why-is-my-classifier-discriminatory | |
| Multi-Layered Gradient Boosting Decision Trees | Ji Feng | https://papers.nips.cc/paper/7614-multi-layered-gradient-boosting-decision-trees | |
| Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning | Tom Zahavy | https://papers.nips.cc/paper/7615-learn-what-not-to-learn-action-elimination-with-deep-reinforcement-learning | |
| Communication Efficient Parallel Algorithms for Optimization on Manifolds | Bayan Saparbayeva | https://papers.nips.cc/paper/7616-communication-efficient-parallel-algorithms-for-optimization-on-manifolds | |
| Neural Code Comprehension: A Learnable Representation of Code Semantics | Tal Ben-Nun | https://papers.nips.cc/paper/7617-neural-code-comprehension-a-learnable-representation-of-code-semantics | |
| Tight Bounds for Collaborative PAC Learning via Multiplicative Weights | Jiecao Chen | https://papers.nips.cc/paper/7618-tight-bounds-for-collaborative-pac-learning-via-multiplicative-weights | |
| BinGAN: Learning Compact Binary Descriptors with a Regularized GAN | Maciej Zieba | https://papers.nips.cc/paper/7619-bingan-learning-compact-binary-descriptors-with-a-regularized-gan | |
| Modern Neural Networks Generalize on Small Data Sets | Matthew Olson | https://papers.nips.cc/paper/7620-modern-neural-networks-generalize-on-small-data-sets | |
| Escaping Saddle Points in Constrained Optimization | Aryan Mokhtari | https://papers.nips.cc/paper/7621-escaping-saddle-points-in-constrained-optimization | |
| Adversarial Attacks on Stochastic Bandits | Kwang-Sung Jun | https://papers.nips.cc/paper/7622-adversarial-attacks-on-stochastic-bandits | |
| Optimal Subsampling with Influence Functions | Daniel Ting | https://papers.nips.cc/paper/7623-optimal-subsampling-with-influence-functions | |
| A Bandit Approach to Sequential Experimental Design with False Discovery Control | Kevin G. Jamieson | https://papers.nips.cc/paper/7624-a-bandit-approach-to-sequential-experimental-design-with-false-discovery-control | |
| Equality of Opportunity in Classification: A Causal Approach | Junzhe Zhang | https://papers.nips.cc/paper/7625-equality-of-opportunity-in-classification-a-causal-approach | |
| Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization | Tianyi Liu | https://papers.nips.cc/paper/7626-towards-understanding-acceleration-tradeoff-between-momentum-and-asynchrony-in-nonconvex-stochastic-optimization | |
| Unsupervised Attention-guided Image-to-Image Translation | Youssef Alami Mejjati | https://papers.nips.cc/paper/7627-unsupervised-attention-guided-image-to-image-translation | |
| Inferring Networks From Random Walk-Based Node Similarities | Jeremy Hoskins | https://papers.nips.cc/paper/7628-inferring-networks-from-random-walk-based-node-similarities | |
| NEON2: Finding Local Minima via First-Order Oracles | Zeyuan Allen-Zhu | https://papers.nips.cc/paper/7629-neon2-finding-local-minima-via-first-order-oracles | |
| Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization | Sijia Liu | https://papers.nips.cc/paper/7630-zeroth-order-stochastic-variance-reduction-for-nonconvex-optimization | |
| Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting | Hippolyt Ritter | https://papers.nips.cc/paper/7631-online-structured-laplace-approximations-for-overcoming-catastrophic-forgetting | |
| DeepProbLog: Neural Probabilistic Logic Programming | Robin Manhaeve | https://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming | |
| Convergence of Cubic Regularization for Nonconvex Optimization under KL Property | Yi Zhou | https://papers.nips.cc/paper/7633-convergence-of-cubic-regularization-for-nonconvex-optimization-under-kl-property | |
| Direct Estimation of Differences in Causal Graphs | Yuhao Wang | https://papers.nips.cc/paper/7634-direct-estimation-of-differences-in-causal-graphs | |
| Sublinear Time Low-Rank Approximation of Distance Matrices | Ainesh Bakshi | https://papers.nips.cc/paper/7635-sublinear-time-low-rank-approximation-of-distance-matrices | |
| Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms | Ganesh Sundaramoorthi | https://papers.nips.cc/paper/7636-variational-pdes-for-acceleration-on-manifolds-and-application-to-diffeomorphisms | |
| Bayesian Inference of Temporal Task Specifications from Demonstrations | Ankit Shah | https://papers.nips.cc/paper/7637-bayesian-inference-of-temporal-task-specifications-from-demonstrations | |
| Data center cooling using model-predictive control | Nevena Lazic | https://papers.nips.cc/paper/7638-data-center-cooling-using-model-predictive-control | |
| Acceleration through Optimistic No-Regret Dynamics | Jun-Kun Wang | https://papers.nips.cc/paper/7639-acceleration-through-optimistic-no-regret-dynamics | |
| Lipschitz regularity of deep neural networks: analysis and efficient estimation | Aladin Virmaux | https://papers.nips.cc/paper/7640-lipschitz-regularity-of-deep-neural-networks-analysis-and-efficient-estimation | |
| Minimax Estimation of Neural Net Distance | Kaiyi Ji | https://papers.nips.cc/paper/7641-minimax-estimation-of-neural-net-distance | |
| Leveraging the Exact Likelihood of Deep Latent Variable Models | Pierre-Alexandre Mattei | https://papers.nips.cc/paper/7642-leveraging-the-exact-likelihood-of-deep-latent-variable-models | |
| Bipartite Stochastic Block Models with Tiny Clusters | Stefan Neumann | https://papers.nips.cc/paper/7643-bipartite-stochastic-block-models-with-tiny-clusters | |
| Learning sparse neural networks via sensitivity-driven regularization | Enzo Tartaglione | https://papers.nips.cc/paper/7644-learning-sparse-neural-networks-via-sensitivity-driven-regularization | |
| Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization | Xiaoxuan Zhang | https://papers.nips.cc/paper/7645-faster-online-learning-of-optimal-threshold-for-consistent-f-measure-optimization | |
| Direct Runge-Kutta Discretization Achieves Acceleration | Jingzhao Zhang | https://papers.nips.cc/paper/7646-direct-runge-kutta-discretization-achieves-acceleration | |
| Adversarial Examples that Fool both Computer Vision and Time-Limited Humans | Gamaleldin Elsayed | https://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans | |
| Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization | Dongruo Zhou | https://papers.nips.cc/paper/7648-stochastic-nested-variance-reduced-gradient-descent-for-nonconvex-optimization | |
| Faster Neural Networks Straight from JPEG | Lionel Gueguen | https://papers.nips.cc/paper/7649-faster-neural-networks-straight-from-jpeg | |
| TopRank: A practical algorithm for online stochastic ranking | Tor Lattimore | https://papers.nips.cc/paper/7650-toprank-a-practical-algorithm-for-online-stochastic-ranking | |
| Learning from discriminative feature feedback | Sanjoy Dasgupta | https://papers.nips.cc/paper/7651-learning-from-discriminative-feature-feedback | |
| RetGK: Graph Kernels based on Return Probabilities of Random Walks | Zhen Zhang | https://papers.nips.cc/paper/7652-retgk-graph-kernels-based-on-return-probabilities-of-random-walks | |
| Deep Generative Markov State Models | Hao Wu | https://papers.nips.cc/paper/7653-deep-generative-markov-state-models | |
| Early Stopping for Nonparametric Testing | Meimei Liu | https://papers.nips.cc/paper/7654-early-stopping-for-nonparametric-testing | |
| Solving Non-smooth Constrained Programs with Lower Complexity than \mathcal{O}(1/\varepsilon): A Primal-Dual Homotopy Smoothing Approach | Xiaohan Wei | https://papers.nips.cc/paper/7655-solving-non-smooth-constrained-programs-with-lower-complexity-than-mathcalo1varepsilon-a-primal-dual-homotopy-smoothing-approach | |
| Heterogeneous Bitwidth Binarization in Convolutional Neural Networks | Joshua Fromm | https://papers.nips.cc/paper/7656-heterogeneous-bitwidth-binarization-in-convolutional-neural-networks | |
| Unsupervised Learning of Object Landmarks through Conditional Image Generation | Tomas Jakab | https://papers.nips.cc/paper/7657-unsupervised-learning-of-object-landmarks-through-conditional-image-generation | |
| Probabilistic Neural Programmed Networks for Scene Generation | Zhiwei Deng | https://papers.nips.cc/paper/7658-probabilistic-neural-programmed-networks-for-scene-generation | |
| The streaming rollout of deep networks - towards fully model-parallel execution | Volker Fischer | https://papers.nips.cc/paper/7659-the-streaming-rollout-of-deep-networks-towards-fully-model-parallel-execution | |
| KONG: Kernels for ordered-neighborhood graphs | Moez Draief | https://papers.nips.cc/paper/7660-kong-kernels-for-ordered-neighborhood-graphs | |
| GumBolt: Extending Gumbel trick to Boltzmann priors | Amir H. Khoshaman | https://papers.nips.cc/paper/7661-gumbolt-extending-gumbel-trick-to-boltzmann-priors | |
| Neural Networks Trained to Solve Differential Equations Learn General Representations | Martin Magill | https://papers.nips.cc/paper/7662-neural-networks-trained-to-solve-differential-equations-learn-general-representations | |
| Beauty-in-averageness and its contextual modulations: A Bayesian statistical account | Chaitanya Ryali | https://papers.nips.cc/paper/7663-beauty-in-averageness-and-its-contextual-modulations-a-bayesian-statistical-account | |
| Distributed Weight Consolidation: A Brain Segmentation Case Study | Patrick McClure | https://papers.nips.cc/paper/7664-distributed-weight-consolidation-a-brain-segmentation-case-study | |
| Efficient Projection onto the Perfect Phylogeny Model | Bei Jia | https://papers.nips.cc/paper/7665-efficient-projection-onto-the-perfect-phylogeny-model | |
| TETRIS: TilE-matching the TRemendous Irregular Sparsity | Yu Ji | https://papers.nips.cc/paper/7666-tetris-tile-matching-the-tremendous-irregular-sparsity | |
| Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification | Harsh Shrivastava | https://papers.nips.cc/paper/7667-cooperative-neural-networks-conn-exploiting-prior-independence-structure-for-improved-classification | |
| Differentially Private Robust Low-Rank Approximation | Raman Arora | https://papers.nips.cc/paper/7668-differentially-private-robust-low-rank-approximation | |
| Meta-Learning MCMC Proposals | Tongzhou Wang | https://papers.nips.cc/paper/7669-meta-learning-mcmc-proposals | |
| An Information-Theoretic Analysis for Thompson Sampling with Many Actions | Shi Dong | https://papers.nips.cc/paper/7670-an-information-theoretic-analysis-for-thompson-sampling-with-many-actions | |
| Flexible and accurate inference and learning for deep generative models | Eszter Vértes | https://papers.nips.cc/paper/7671-flexible-and-accurate-inference-and-learning-for-deep-generative-models | |
| The Price of Privacy for Low-rank Factorization | Jalaj Upadhyay | https://papers.nips.cc/paper/7672-the-price-of-privacy-for-low-rank-factorization | |
| Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator | Sarah Dean | https://papers.nips.cc/paper/7673-regret-bounds-for-robust-adaptive-control-of-the-linear-quadratic-regulator | |
| Bilevel Distance Metric Learning for Robust Image Recognition | Jie Xu | https://papers.nips.cc/paper/7674-bilevel-distance-metric-learning-for-robust-image-recognition | |
| Differentially Private Uniformly Most Powerful Tests for Binomial Data | Jordan Awan | https://papers.nips.cc/paper/7675-differentially-private-uniformly-most-powerful-tests-for-binomial-data | |
| Scalable Coordinated Exploration in Concurrent Reinforcement Learning | Maria Dimakopoulou | https://papers.nips.cc/paper/7676-scalable-coordinated-exploration-in-concurrent-reinforcement-learning | |
| Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models | Amir Dezfouli | https://papers.nips.cc/paper/7677-integrated-accounts-of-behavioral-and-neuroimaging-data-using-flexible-recurrent-neural-network-models | |
| BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training | Songtao Wang | https://papers.nips.cc/paper/7678-bml-a-high-performance-low-cost-gradient-synchronization-algorithm-for-dml-training | |
| Inexact trust-region algorithms on Riemannian manifolds | Hiroyuki Kasai | https://papers.nips.cc/paper/7679-inexact-trust-region-algorithms-on-riemannian-manifolds | |
| Can We Gain More from Orthogonality Regularizations in Training Deep Networks? | Nitin Bansal | https://papers.nips.cc/paper/7680-can-we-gain-more-from-orthogonality-regularizations-in-training-deep-networks | |
| Binary Rating Estimation with Graph Side Information | Kwangjun Ahn | https://papers.nips.cc/paper/7681-binary-rating-estimation-with-graph-side-information | |
| SimplE Embedding for Link Prediction in Knowledge Graphs | Seyed Mehran Kazemi | https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs | |
| Differentially Private Contextual Linear Bandits | Roshan Shariff | https://papers.nips.cc/paper/7683-differentially-private-contextual-linear-bandits | |
| Submodular Field Grammars: Representation, Inference, and Application to Image Parsing | Abram L. Friesen | https://papers.nips.cc/paper/7684-submodular-field-grammars-representation-inference-and-application-to-image-parsing | |
| A Bridging Framework for Model Optimization and Deep Propagation | Risheng Liu | https://papers.nips.cc/paper/7685-a-bridging-framework-for-model-optimization-and-deep-propagation | |
| Completing State Representations using Spectral Learning | Nan Jiang | https://papers.nips.cc/paper/7686-completing-state-representations-using-spectral-learning | |
| Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates | Yining Wang | https://papers.nips.cc/paper/7687-optimization-of-smooth-functions-with-noisy-observations-local-minimax-rates | |
| Adding One Neuron Can Eliminate All Bad Local Minima | SHIYU LIANG | https://papers.nips.cc/paper/7688-adding-one-neuron-can-eliminate-all-bad-local-minima | |
| Mean-field theory of graph neural networks in graph partitioning | Tatsuro Kawamoto | https://papers.nips.cc/paper/7689-mean-field-theory-of-graph-neural-networks-in-graph-partitioning | |
| The Physical Systems Behind Optimization Algorithms | Lin Yang | https://papers.nips.cc/paper/7690-the-physical-systems-behind-optimization-algorithms | |
| Mallows Models for Top-k Lists | Flavio Chierichetti | https://papers.nips.cc/paper/7691-mallows-models-for-top-k-lists | |
| Amortized Inference Regularization | Rui Shu | https://papers.nips.cc/paper/7692-amortized-inference-regularization | |
| Maximum Causal Tsallis Entropy Imitation Learning | Kyungjae Lee | https://papers.nips.cc/paper/7693-maximum-causal-tsallis-entropy-imitation-learning | |
| Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives | Song Zhou | https://papers.nips.cc/paper/7694-limited-memory-kelleys-method-converges-for-composite-convex-and-submodular-objectives | |
| Semi-Supervised Learning with Declaratively Specified Entropy Constraints | Haitian Sun | https://papers.nips.cc/paper/7695-semi-supervised-learning-with-declaratively-specified-entropy-constraints | |
| End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems | Linfeng Zhang | https://papers.nips.cc/paper/7696-end-to-end-symmetry-preserving-inter-atomic-potential-energy-model-for-finite-and-extended-systems | |
| Sparsified SGD with Memory | Sebastian U. Stich | https://papers.nips.cc/paper/7697-sparsified-sgd-with-memory | |
| Exponentiated Strongly Rayleigh Distributions | Zelda E. Mariet | https://papers.nips.cc/paper/7698-exponentiated-strongly-rayleigh-distributions | |
| Importance Weighting and Variational Inference | Justin Domke | https://papers.nips.cc/paper/7699-importance-weighting-and-variational-inference | |
| Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | Ye Jia | https://papers.nips.cc/paper/7700-transfer-learning-from-speaker-verification-to-multispeaker-text-to-speech-synthesis | |
| Expanding Holographic Embeddings for Knowledge Completion | Yexiang Xue | https://papers.nips.cc/paper/7701-expanding-holographic-embeddings-for-knowledge-completion | |
| Lifelong Inverse Reinforcement Learning | Jorge Armando Mendez Mendez | https://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning | |
| Explaining Deep Learning Models -- A Bayesian Non-parametric Approach | Wenbo Guo | https://papers.nips.cc/paper/7703-explaining-deep-learning-models-a-bayesian-non-parametric-approach | |
| Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima | Yaodong Yu | https://papers.nips.cc/paper/7704-third-order-smoothness-helps-faster-stochastic-optimization-algorithms-for-finding-local-minima | |
| COLA: Decentralized Linear Learning | Lie He | https://papers.nips.cc/paper/7705-cola-decentralized-linear-learning | |
| MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare | Edward Choi | https://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare | |
| Adaptive Sampling Towards Fast Graph Representation Learning | Wenbing Huang | https://papers.nips.cc/paper/7707-adaptive-sampling-towards-fast-graph-representation-learning | |
| Hunting for Discriminatory Proxies in Linear Regression Models | Samuel Yeom | https://papers.nips.cc/paper/7708-hunting-for-discriminatory-proxies-in-linear-regression-models | |
| Towards Robust Detection of Adversarial Examples | Tianyu Pang | https://papers.nips.cc/paper/7709-towards-robust-detection-of-adversarial-examples | |
| Active Matting | Xin Yang | https://papers.nips.cc/paper/7710-active-matting | |
| Learning filter widths of spectral decompositions with wavelets | Haidar Khan | https://papers.nips.cc/paper/7711-learning-filter-widths-of-spectral-decompositions-with-wavelets | |
| Byzantine Stochastic Gradient Descent | Dan Alistarh | https://papers.nips.cc/paper/7712-byzantine-stochastic-gradient-descent | |
| PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits | Bianca Dumitrascu | https://papers.nips.cc/paper/7713-pg-ts-improved-thompson-sampling-for-logistic-contextual-bandits | |
| Spectral Filtering for General Linear Dynamical Systems | Elad Hazan | https://papers.nips.cc/paper/7714-spectral-filtering-for-general-linear-dynamical-systems | |
| On Learning Intrinsic Rewards for Policy Gradient Methods | Zeyu Zheng | https://papers.nips.cc/paper/7715-on-learning-intrinsic-rewards-for-policy-gradient-methods | |
| Boolean Decision Rules via Column Generation | Sanjeeb Dash | https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation | |
| Adversarial Text Generation via Feature-Mover's Distance | Liqun Chen | https://papers.nips.cc/paper/7717-adversarial-text-generation-via-feature-movers-distance | |
| Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions | Mingrui Liu | https://papers.nips.cc/paper/7718-fast-rates-of-erm-and-stochastic-approximation-adaptive-to-error-bound-conditions | |
| Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels | Shahin Shahrampour | https://papers.nips.cc/paper/7719-learning-bounds-for-greedy-approximation-with-explicit-feature-maps-from-multiple-kernels | |
| A Mathematical Model For Optimal Decisions In A Representative Democracy | Malik Magdon-Ismail | https://papers.nips.cc/paper/7720-a-mathematical-model-for-optimal-decisions-in-a-representative-democracy | |
| Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making | Nishant Desai | https://papers.nips.cc/paper/7721-negotiable-reinforcement-learning-for-pareto-optimal-sequential-decision-making | |
| Non-metric Similarity Graphs for Maximum Inner Product Search | Stanislav Morozov | https://papers.nips.cc/paper/7722-non-metric-similarity-graphs-for-maximum-inner-product-search | |
| Recurrently Controlled Recurrent Networks | Yi Tay | https://papers.nips.cc/paper/7723-recurrently-controlled-recurrent-networks | |
| Fast greedy algorithms for dictionary selection with generalized sparsity constraints | Kaito Fujii | https://papers.nips.cc/paper/7724-fast-greedy-algorithms-for-dictionary-selection-with-generalized-sparsity-constraints | |
| Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models | Kurtland Chua | https://papers.nips.cc/paper/7725-deep-reinforcement-learning-in-a-handful-of-trials-using-probabilistic-dynamics-models | |
| A Smoother Way to Train Structured Prediction Models | Venkata Krishna Pillutla | https://papers.nips.cc/paper/7726-a-smoother-way-to-train-structured-prediction-models | |
| Context-dependent upper-confidence bounds for directed exploration | Raksha Kumaraswamy | https://papers.nips.cc/paper/7727-context-dependent-upper-confidence-bounds-for-directed-exploration | |
| A Unified View of Piecewise Linear Neural Network Verification | Rudy R. Bunel | https://papers.nips.cc/paper/7728-a-unified-view-of-piecewise-linear-neural-network-verification | |
| Hierarchical Graph Representation Learning with Differentiable Pooling | Zhitao Ying | https://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling | |
| Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates | Quoc Tran Dinh | https://papers.nips.cc/paper/7730-non-ergodic-alternating-proximal-augmented-lagrangian-algorithms-with-optimal-rates | |
| Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces | Boyla Mainsah | https://papers.nips.cc/paper/7731-information-based-adaptive-stimulus-selection-to-optimize-communication-efficiency-in-brain-computer-interfaces | |
| Porcupine Neural Networks: Approximating Neural Network Landscapes | Soheil Feizi | https://papers.nips.cc/paper/7732-porcupine-neural-networks-approximating-neural-network-landscapes | |
| Fairness Through Computationally-Bounded Awareness | Michael Kim | https://papers.nips.cc/paper/7733-fairness-through-computationally-bounded-awareness | |
| Adaptive Negative Curvature Descent with Applications in Non-convex Optimization | Mingrui Liu | https://papers.nips.cc/paper/7734-adaptive-negative-curvature-descent-with-applications-in-non-convex-optimization | |
| Is Q-Learning Provably Efficient? | Chi Jin | https://papers.nips.cc/paper/7735-is-q-learning-provably-efficient | |
| Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections | Xin Zhang | https://papers.nips.cc/paper/7736-interpreting-neural-network-judgments-via-minimal-stable-and-symbolic-corrections | |
| Measures of distortion for machine learning | Leena Chennuru Vankadara | https://papers.nips.cc/paper/7737-measures-of-distortion-for-machine-learning | |
| On the Local Minima of the Empirical Risk | Chi Jin | https://papers.nips.cc/paper/7738-on-the-local-minima-of-the-empirical-risk | |
| Densely Connected Attention Propagation for Reading Comprehension | Yi Tay | https://papers.nips.cc/paper/7739-densely-connected-attention-propagation-for-reading-comprehension | |
| Bandit Learning with Positive Externalities | Virag Shah | https://papers.nips.cc/paper/7740-bandit-learning-with-positive-externalities | |
| Learning Confidence Sets using Support Vector Machines | Wenbo Wang | https://papers.nips.cc/paper/7741-learning-confidence-sets-using-support-vector-machines | |
| Efficient Neural Network Robustness Certification with General Activation Functions | Huan Zhang | https://papers.nips.cc/paper/7742-efficient-neural-network-robustness-certification-with-general-activation-functions | |
| Hessian-based Analysis of Large Batch Training and Robustness to Adversaries | Zhewei Yao | https://papers.nips.cc/paper/7743-hessian-based-analysis-of-large-batch-training-and-robustness-to-adversaries | |
| Neural Edit Operations for Biological Sequences | Satoshi Koide | https://papers.nips.cc/paper/7744-neural-edit-operations-for-biological-sequences | |
| Objective and efficient inference for couplings in neuronal networks | Yu Terada | https://papers.nips.cc/paper/7745-objective-and-efficient-inference-for-couplings-in-neuronal-networks | |
| Learning from Group Comparisons: Exploiting Higher Order Interactions | Yao Li | https://papers.nips.cc/paper/7746-learning-from-group-comparisons-exploiting-higher-order-interactions | |
| Supervising Unsupervised Learning | Vikas Garg | https://papers.nips.cc/paper/7747-supervising-unsupervised-learning | |
| Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks | Quan Zhang | https://papers.nips.cc/paper/7748-nonparametric-bayesian-lomax-delegate-racing-for-survival-analysis-with-competing-risks | |
| Adversarially Robust Generalization Requires More Data | Ludwig Schmidt | https://papers.nips.cc/paper/7749-adversarially-robust-generalization-requires-more-data | |
| Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents | Edoardo Conti | https://papers.nips.cc/paper/7750-improving-exploration-in-evolution-strategies-for-deep-reinforcement-learning-via-a-population-of-novelty-seeking-agents | |
| Practical exact algorithm for trembling-hand equilibrium refinements in games | Gabriele Farina | https://papers.nips.cc/paper/7751-practical-exact-algorithm-for-trembling-hand-equilibrium-refinements-in-games | |
| LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning | Tianyi Chen | https://papers.nips.cc/paper/7752-lag-lazily-aggregated-gradient-for-communication-efficient-distributed-learning | |
| Scalable Robust Matrix Factorization with Nonconvex Loss | Quanming Yao | https://papers.nips.cc/paper/7753-scalable-robust-matrix-factorization-with-nonconvex-loss | |
| Power-law efficient neural codes provide general link between perceptual bias and discriminability | Michael Morais | https://papers.nips.cc/paper/7754-power-law-efficient-neural-codes-provide-general-link-between-perceptual-bias-and-discriminability | |
| Geometry-Aware Recurrent Neural Networks for Active Visual Recognition | Ricson Cheng | https://papers.nips.cc/paper/7755-geometry-aware-recurrent-neural-networks-for-active-visual-recognition | |
| Unsupervised Adversarial Invariance | Ayush Jaiswal | https://papers.nips.cc/paper/7756-unsupervised-adversarial-invariance | |
| Content preserving text generation with attribute controls | Lajanugen Logeswaran | https://papers.nips.cc/paper/7757-content-preserving-text-generation-with-attribute-controls | |
| Multi-armed Bandits with Compensation | Siwei Wang | https://papers.nips.cc/paper/7758-multi-armed-bandits-with-compensation | |
| GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training | Mingchao Yu | https://papers.nips.cc/paper/7759-gradiveq-vector-quantization-for-bandwidth-efficient-gradient-aggregation-in-distributed-cnn-training | |
| Learning in Games with Lossy Feedback | Zhengyuan Zhou | https://papers.nips.cc/paper/7760-learning-in-games-with-lossy-feedback | |
| Scalable methods for 8-bit training of neural networks | Ron Banner | https://papers.nips.cc/paper/7761-scalable-methods-for-8-bit-training-of-neural-networks | |
| Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization | Zhihui Zhu | https://papers.nips.cc/paper/7762-dropping-symmetry-for-fast-symmetric-nonnegative-matrix-factorization | |
| Link Prediction Based on Graph Neural Networks | Muhan Zhang | https://papers.nips.cc/paper/7763-link-prediction-based-on-graph-neural-networks | |
| Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task | Dalin Guo | https://papers.nips.cc/paper/7764-why-so-gloomy-a-bayesian-explanation-of-human-pessimism-bias-in-the-multi-armed-bandit-task | |
| Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model | Aaron Sidford | https://papers.nips.cc/paper/7765-near-optimal-time-and-sample-complexities-for-solving-markov-decision-processes-with-a-generative-model | |
| ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions | Hongyang Gao | https://papers.nips.cc/paper/7766-channelnets-compact-and-efficient-convolutional-neural-networks-via-channel-wise-convolutions | |
| Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models | Shoubo Hu | https://papers.nips.cc/paper/7767-causal-inference-and-mechanism-clustering-of-a-mixture-of-additive-noise-models | |
| Contour location via entropy reduction leveraging multiple information sources | Alexandre Marques | https://papers.nips.cc/paper/7768-contour-location-via-entropy-reduction-leveraging-multiple-information-sources | |
| Assessing Generative Models via Precision and Recall | Mehdi S. M. Sajjadi | https://papers.nips.cc/paper/7769-assessing-generative-models-via-precision-and-recall | |
| Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning | Yonathan Efroni | https://papers.nips.cc/paper/7770-multiple-step-greedy-policies-in-approximate-and-online-reinforcement-learning | |
| A Convex Duality Framework for GANs | Farzan Farnia | https://papers.nips.cc/paper/7771-a-convex-duality-framework-for-gans | |
| Horizon-Independent Minimax Linear Regression | Alan Malek | https://papers.nips.cc/paper/7772-horizon-independent-minimax-linear-regression | |
| Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression | Neha Gupta | https://papers.nips.cc/paper/7773-exploiting-numerical-sparsity-for-efficient-learning-faster-eigenvector-computation-and-regression | |
| Experimental Design for Cost-Aware Learning of Causal Graphs | Erik Lindgren | https://papers.nips.cc/paper/7774-experimental-design-for-cost-aware-learning-of-causal-graphs | |
| Task-Driven Convolutional Recurrent Models of the Visual System | Aran Nayebi | https://papers.nips.cc/paper/7775-task-driven-convolutional-recurrent-models-of-the-visual-system | |
| Meta-Reinforcement Learning of Structured Exploration Strategies | Abhishek Gupta | https://papers.nips.cc/paper/7776-meta-reinforcement-learning-of-structured-exploration-strategies | |
| Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation | Tomoya Murata | https://papers.nips.cc/paper/7777-sample-efficient-stochastic-gradient-iterative-hard-thresholding-method-for-stochastic-sparse-linear-regression-with-limited-attribute-observation | |
| Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance | Neal Jean | https://papers.nips.cc/paper/7778-semi-supervised-deep-kernel-learning-regression-with-unlabeled-data-by-minimizing-predictive-variance | |
| Generalizing to Unseen Domains via Adversarial Data Augmentation | Riccardo Volpi | https://papers.nips.cc/paper/7779-generalizing-to-unseen-domains-via-adversarial-data-augmentation | |
| Hyperbolic Neural Networks | Octavian Ganea | https://papers.nips.cc/paper/7780-hyperbolic-neural-networks | |
| Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation | Qiang Liu | https://papers.nips.cc/paper/7781-breaking-the-curse-of-horizon-infinite-horizon-off-policy-estimation | |
| Learning Task Specifications from Demonstrations | Marcell Vazquez-Chanlatte | https://papers.nips.cc/paper/7782-learning-task-specifications-from-demonstrations | |
| Learning a latent manifold of odor representations from neural responses in piriform cortex | Anqi Wu | https://papers.nips.cc/paper/7783-learning-a-latent-manifold-of-odor-representations-from-neural-responses-in-piriform-cortex | |
| Fully Understanding The Hashing Trick | Lior Kamma | https://papers.nips.cc/paper/7784-fully-understanding-the-hashing-trick | |
| Evolved Policy Gradients | Rein Houthooft | https://papers.nips.cc/paper/7785-evolved-policy-gradients | |
| The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network | Jeffrey Pennington | https://papers.nips.cc/paper/7786-the-spectrum-of-the-fisher-information-matrix-of-a-single-hidden-layer-neural-network | |
| Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra | John T. Halloran | https://papers.nips.cc/paper/7787-learning-concave-conditional-likelihood-models-for-improved-analysis-of-tandem-mass-spectra | |
| Differentially Private k-Means with Constant Multiplicative Error | Uri Stemmer | https://papers.nips.cc/paper/7788-differentially-private-k-means-with-constant-multiplicative-error | |
| Policy Optimization via Importance Sampling | Alberto Maria Metelli | https://papers.nips.cc/paper/7789-policy-optimization-via-importance-sampling | |
| Estimating Learnability in the Sublinear Data Regime | Weihao Kong | https://papers.nips.cc/paper/7790-estimating-learnability-in-the-sublinear-data-regime | |
| Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation | Shivapratap Gopakumar | https://papers.nips.cc/paper/7791-algorithmic-assurance-an-active-approach-to-algorithmic-testing-using-bayesian-optimisation | |
| Community Exploration: From Offline Optimization to Online Learning | Xiaowei Chen | https://papers.nips.cc/paper/7792-community-exploration-from-offline-optimization-to-online-learning | |
| A Dual Framework for Low-rank Tensor Completion | Madhav Nimishakavi | https://papers.nips.cc/paper/7793-a-dual-framework-for-low-rank-tensor-completion | |
| Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames | Geneviève Robin | https://papers.nips.cc/paper/7794-low-rank-interaction-with-sparse-additive-effects-model-for-large-data-frames | |
| Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing | Zehong Hu | https://papers.nips.cc/paper/7795-inference-aided-reinforcement-learning-for-incentive-mechanism-design-in-crowdsourcing | |
| Middle-Out Decoding | Shikib Mehri | https://papers.nips.cc/paper/7796-middle-out-decoding | |
| First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time | Yi Xu | https://papers.nips.cc/paper/7797-first-order-stochastic-algorithms-for-escaping-from-saddle-points-in-almost-linear-time | |
| To Trust Or Not To Trust A Classifier | Heinrich Jiang | https://papers.nips.cc/paper/7798-to-trust-or-not-to-trust-a-classifier | |
| Reparameterization Gradient for Non-differentiable Models | Wonyeol Lee | https://papers.nips.cc/paper/7799-reparameterization-gradient-for-non-differentiable-models | |
| A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization | Zhize Li | https://papers.nips.cc/paper/7800-a-simple-proximal-stochastic-gradient-method-for-nonsmooth-nonconvex-optimization | |
| Multimodal Generative Models for Scalable Weakly-Supervised Learning | Mike Wu | https://papers.nips.cc/paper/7801-multimodal-generative-models-for-scalable-weakly-supervised-learning | |
| How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? | Richard Zhang | https://papers.nips.cc/paper/7802-how-much-restricted-isometry-is-needed-in-nonconvex-matrix-recovery | |
| Occam's razor is insufficient to infer the preferences of irrational agents | Stuart Armstrong | https://papers.nips.cc/paper/7803-occams-razor-is-insufficient-to-infer-the-preferences-of-irrational-agents | |
| Manifold Structured Prediction | Alessandro Rudi | https://papers.nips.cc/paper/7804-manifold-structured-prediction | |
| Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity | Laming Chen | https://papers.nips.cc/paper/7805-fast-greedy-map-inference-for-determinantal-point-process-to-improve-recommendation-diversity | |
| Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs | Yanlin Han | https://papers.nips.cc/paper/7806-learning-others-intentional-models-in-multi-agent-settings-using-interactive-pomdps | |
| Contextual Pricing for Lipschitz Buyers | Jieming Mao | https://papers.nips.cc/paper/7807-contextual-pricing-for-lipschitz-buyers | |
| Online Improper Learning with an Approximation Oracle | Elad Hazan | https://papers.nips.cc/paper/7808-online-improper-learning-with-an-approximation-oracle | |
| Bandit Learning in Concave N-Person Games | Mario Bravo | https://papers.nips.cc/paper/7809-bandit-learning-in-concave-n-person-games | |
| On Fast Leverage Score Sampling and Optimal Learning | Alessandro Rudi | https://papers.nips.cc/paper/7810-on-fast-leverage-score-sampling-and-optimal-learning | |
| Unsupervised Video Object Segmentation for Deep Reinforcement Learning | Vikash Goel | https://papers.nips.cc/paper/7811-unsupervised-video-object-segmentation-for-deep-reinforcement-learning | |
| Efficient inference for time-varying behavior during learning | Nicholas G. Roy | https://papers.nips.cc/paper/7812-efficient-inference-for-time-varying-behavior-during-learning | |
| Learning convex polytopes with margin | Lee-Ad Gottlieb | https://papers.nips.cc/paper/7813-learning-convex-polytopes-with-margin | |
| Critical initialisation for deep signal propagation in noisy rectifier neural networks | Arnu Pretorius | https://papers.nips.cc/paper/7814-critical-initialisation-for-deep-signal-propagation-in-noisy-rectifier-neural-networks | |
| Insights on representational similarity in neural networks with canonical correlation | Ari Morcos | https://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation | |
| Variational Inference with Tail-adaptive f-Divergence | Dilin Wang | https://papers.nips.cc/paper/7816-variational-inference-with-tail-adaptive-f-divergence | |
| Mental Sampling in Multimodal Representations | Jianqiao Zhu | https://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations | |
| Adversarially Robust Optimization with Gaussian Processes | Ilija Bogunovic | https://papers.nips.cc/paper/7818-adversarially-robust-optimization-with-gaussian-processes | |
| Learning to Multitask | Yu Zhang | https://papers.nips.cc/paper/7819-learning-to-multitask | |
| Loss Functions for Multiset Prediction | Sean Welleck | https://papers.nips.cc/paper/7820-loss-functions-for-multiset-prediction | |
| Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d+1)-partite graphs | Gennaro Auricchio | https://papers.nips.cc/paper/7821-computing-kantorovich-wasserstein-distances-on-d-dimensional-histograms-using-d1-partite-graphs | |
| Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability | Michael Tsang | https://papers.nips.cc/paper/7822-neural-interaction-transparency-nit-disentangling-learned-interactions-for-improved-interpretability | |
| CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces | Liheng Zhang | https://papers.nips.cc/paper/7823-cappronet-deep-feature-learning-via-orthogonal-projections-onto-capsule-subspaces | |
| Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence | Trong Dinh Thac Do | https://papers.nips.cc/paper/7824-gamma-poisson-dynamic-matrix-factorization-embedded-with-metadata-influence | |
| Masking: A New Perspective of Noisy Supervision | Bo Han | https://papers.nips.cc/paper/7825-masking-a-new-perspective-of-noisy-supervision | |
| On GANs and GMMs | Eitan Richardson | https://papers.nips.cc/paper/7826-on-gans-and-gmms | |
| Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance | Giulia Luise | https://papers.nips.cc/paper/7827-differential-properties-of-sinkhorn-approximation-for-learning-with-wasserstein-distance | |
| Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching | Stepan Tulyakov | https://papers.nips.cc/paper/7828-practical-deep-stereo-pds-toward-applications-friendly-deep-stereo-matching | |
| A Bayes-Sard Cubature Method | Toni Karvonen | https://papers.nips.cc/paper/7829-a-bayes-sard-cubature-method | |
| Dual Swap Disentangling | Zunlei Feng | https://papers.nips.cc/paper/7830-dual-swap-disentangling | |
| Diverse Ensemble Evolution: Curriculum Data-Model Marriage | Tianyi Zhou | https://papers.nips.cc/paper/7831-diverse-ensemble-evolution-curriculum-data-model-marriage | |
| Binary Classification from Positive-Confidence Data | Takashi Ishida | https://papers.nips.cc/paper/7832-binary-classification-from-positive-confidence-data | |
| Deep Generative Models for Distribution-Preserving Lossy Compression | Michael Tschannen | https://papers.nips.cc/paper/7833-deep-generative-models-for-distribution-preserving-lossy-compression | |
| Exact natural gradient in deep linear networks and its application to the nonlinear case | Alberto Bernacchia | https://papers.nips.cc/paper/7834-exact-natural-gradient-in-deep-linear-networks-and-its-application-to-the-nonlinear-case | |
| Constructing Fast Network through Deconstruction of Convolution | Yunho Jeon | https://papers.nips.cc/paper/7835-constructing-fast-network-through-deconstruction-of-convolution | |
| Memory Replay GANs: Learning to Generate New Categories without Forgetting | Chenshen Wu | https://papers.nips.cc/paper/7836-memory-replay-gans-learning-to-generate-new-categories-without-forgetting | |
| The Convergence of Sparsified Gradient Methods | Dan Alistarh | https://papers.nips.cc/paper/7837-the-convergence-of-sparsified-gradient-methods | |
| Automating Bayesian optimization with Bayesian optimization | Gustavo Malkomes | https://papers.nips.cc/paper/7838-automating-bayesian-optimization-with-bayesian-optimization | |
| Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning | yunlong yu | https://papers.nips.cc/paper/7839-stacked-semantics-guided-attention-model-for-fine-grained-zero-shot-learning | |
| Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification | Dimitrios Milios | https://papers.nips.cc/paper/7840-dirichlet-based-gaussian-processes-for-large-scale-calibrated-classification | |
| Multi-Task Zipping via Layer-wise Neuron Sharing | Xiaoxi He | https://papers.nips.cc/paper/7841-multi-task-zipping-via-layer-wise-neuron-sharing | |
| Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo | Oren Mangoubi | https://papers.nips.cc/paper/7842-dimensionally-tight-bounds-for-second-order-hamiltonian-monte-carlo | |
| Approximation algorithms for stochastic clustering | David Harris | https://papers.nips.cc/paper/7843-approximation-algorithms-for-stochastic-clustering | |
| Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks | Xiaodong Cui | https://papers.nips.cc/paper/7844-evolutionary-stochastic-gradient-descent-for-optimization-of-deep-neural-networks | |
| Learning to Infer Graphics Programs from Hand-Drawn Images | Kevin Ellis | https://papers.nips.cc/paper/7845-learning-to-infer-graphics-programs-from-hand-drawn-images | |
| Graphical Generative Adversarial Networks | Chongxuan LI | https://papers.nips.cc/paper/7846-graphical-generative-adversarial-networks | |
| Variational Learning on Aggregate Outputs with Gaussian Processes | Ho Chung Law | https://papers.nips.cc/paper/7847-variational-learning-on-aggregate-outputs-with-gaussian-processes | |
| MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models | Boyuan Pan | https://papers.nips.cc/paper/7848-macnet-transferring-knowledge-from-machine-comprehension-to-sequence-to-sequence-models | |
| Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks | Ali Shafahi | https://papers.nips.cc/paper/7849-poison-frogs-targeted-clean-label-poisoning-attacks-on-neural-networks | |
| Information Constraints on Auto-Encoding Variational Bayes | Romain Lopez | https://papers.nips.cc/paper/7850-information-constraints-on-auto-encoding-variational-bayes | |
| Recurrent Transformer Networks for Semantic Correspondence | Seungryong Kim | https://papers.nips.cc/paper/7851-recurrent-transformer-networks-for-semantic-correspondence | |
| Online convex optimization for cumulative constraints | Jianjun Yuan | https://papers.nips.cc/paper/7852-online-convex-optimization-for-cumulative-constraints | |
| Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer | David Madras | https://papers.nips.cc/paper/7853-predict-responsibly-improving-fairness-and-accuracy-by-learning-to-defer | |
| Deep State Space Models for Unconditional Word Generation | Florian Schmidt | https://papers.nips.cc/paper/7854-deep-state-space-models-for-unconditional-word-generation | |
| ResNet with one-neuron hidden layers is a Universal Approximator | Hongzhou Lin | https://papers.nips.cc/paper/7855-resnet-with-one-neuron-hidden-layers-is-a-universal-approximator | |
| Transfer of Value Functions via Variational Methods | Andrea Tirinzoni | https://papers.nips.cc/paper/7856-transfer-of-value-functions-via-variational-methods | |
| The Cluster Description Problem - Complexity Results, Formulations and Approximations | Ian Davidson | https://papers.nips.cc/paper/7857-the-cluster-description-problem-complexity-results-formulations-and-approximations | |
| Sharp Bounds for Generalized Uniformity Testing | Ilias Diakonikolas | https://papers.nips.cc/paper/7858-sharp-bounds-for-generalized-uniformity-testing | |
| Deep Neural Networks with Box Convolutions | Egor Burkov | https://papers.nips.cc/paper/7859-deep-neural-networks-with-box-convolutions | |
| Learning towards Minimum Hyperspherical Energy | Weiyang Liu | https://papers.nips.cc/paper/7860-learning-towards-minimum-hyperspherical-energy | |
| LF-Net: Learning Local Features from Images | Yuki Ono | https://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images | |
| SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient | Aaron Mishkin | https://papers.nips.cc/paper/7862-slang-fast-structured-covariance-approximations-for-bayesian-deep-learning-with-natural-gradient | |
| Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming | Bart van Merrienboer | https://papers.nips.cc/paper/7863-tangent-automatic-differentiation-using-source-code-transformation-for-dynamically-typed-array-programming | |
| Multi-domain Causal Structure Learning in Linear Systems | AmirEmad Ghassami | https://papers.nips.cc/paper/7864-multi-domain-causal-structure-learning-in-linear-systems | |
| Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences | Borja Balle | https://papers.nips.cc/paper/7865-privacy-amplification-by-subsampling-tight-analyses-via-couplings-and-divergences | |
| Exponentially Weighted Imitation Learning for Batched Historical Data | Qing Wang | https://papers.nips.cc/paper/7866-exponentially-weighted-imitation-learning-for-batched-historical-data | |
| Algebraic tests of general Gaussian latent tree models | Dennis Leung | https://papers.nips.cc/paper/7867-algebraic-tests-of-general-gaussian-latent-tree-models | |
| Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models | Minjia Zhang | https://papers.nips.cc/paper/7868-navigating-with-graph-representations-for-fast-and-scalable-decoding-of-neural-language-models | |
| Deep Structured Prediction with Nonlinear Output Transformations | Colin Graber | https://papers.nips.cc/paper/7869-deep-structured-prediction-with-nonlinear-output-transformations | |
| Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling | Emilie Kaufmann | https://papers.nips.cc/paper/7870-sequential-test-for-the-lowest-mean-from-thompson-to-murphy-sampling | |
| Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization | Bargav Jayaraman | https://papers.nips.cc/paper/7871-distributed-learning-without-distress-privacy-preserving-empirical-risk-minimization | |
| A no-regret generalization of hierarchical softmax to extreme multi-label classification | Marek Wydmuch | https://papers.nips.cc/paper/7872-a-no-regret-generalization-of-hierarchical-softmax-to-extreme-multi-label-classification | |
| Efficient Formal Safety Analysis of Neural Networks | Shiqi Wang | https://papers.nips.cc/paper/7873-efficient-formal-safety-analysis-of-neural-networks | |
| Bayesian Distributed Stochastic Gradient Descent | Michael Teng | https://papers.nips.cc/paper/7874-bayesian-distributed-stochastic-gradient-descent | |
| Visualizing the Loss Landscape of Neural Nets | Hao Li | https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets | |
| The Limits of Post-Selection Generalization | Jonathan Ullman | https://papers.nips.cc/paper/7876-the-limits-of-post-selection-generalization | |
| Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | Jiaxuan You | https://papers.nips.cc/paper/7877-graph-convolutional-policy-network-for-goal-directed-molecular-graph-generation | |
| On Controllable Sparse Alternatives to Softmax | Anirban Laha | https://papers.nips.cc/paper/7878-on-controllable-sparse-alternatives-to-softmax | |
| L4: Practical loss-based stepsize adaptation for deep learning | Michal Rolinek | https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning | |
| Learning Latent Subspaces in Variational Autoencoders | Jack Klys | https://papers.nips.cc/paper/7880-learning-latent-subspaces-in-variational-autoencoders | |
| Turbo Learning for CaptionBot and DrawingBot | Qiuyuan Huang | https://papers.nips.cc/paper/7881-turbo-learning-for-captionbot-and-drawingbot | |
| Learning to Teach with Dynamic Loss Functions | Lijun Wu | https://papers.nips.cc/paper/7882-learning-to-teach-with-dynamic-loss-functions | |
| Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation | Edward Smith | https://papers.nips.cc/paper/7883-multi-view-silhouette-and-depth-decomposition-for-high-resolution-3d-object-representation | |
| Size-Noise Tradeoffs in Generative Networks | Bolton Bailey | https://papers.nips.cc/paper/7884-size-noise-tradeoffs-in-generative-networks | |
| Online Adaptive Methods, Universality and Acceleration | Yehuda Kfir Levy | https://papers.nips.cc/paper/7885-online-adaptive-methods-universality-and-acceleration | |
| Compact Generalized Non-local Network | Kaiyu Yue | https://papers.nips.cc/paper/7886-compact-generalized-non-local-network | |
| On the Local Hessian in Back-propagation | Huishuai Zhang | https://papers.nips.cc/paper/7887-on-the-local-hessian-in-back-propagation | |
| The Everlasting Database: Statistical Validity at a Fair Price | Blake E. Woodworth | https://papers.nips.cc/paper/7888-the-everlasting-database-statistical-validity-at-a-fair-price | |
| Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks | Yusuke Tsuzuku | https://papers.nips.cc/paper/7889-lipschitz-margin-training-scalable-certification-of-perturbation-invariance-for-deep-neural-networks | |
| Proximal SCOPE for Distributed Sparse Learning | Shenyi Zhao | https://papers.nips.cc/paper/7890-proximal-scope-for-distributed-sparse-learning | |
| On Coresets for Logistic Regression | Alexander Munteanu | https://papers.nips.cc/paper/7891-on-coresets-for-logistic-regression | |
| Neural Ordinary Differential Equations | Tian Qi Chen | https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations | |
| Unsupervised Learning of Artistic Styles with Archetypal Style Analysis | Daan Wynen | https://papers.nips.cc/paper/7893-unsupervised-learning-of-artistic-styles-with-archetypal-style-analysis | |
| Approximating Real-Time Recurrent Learning with Random Kronecker Factors | Asier Mujika | https://papers.nips.cc/paper/7894-approximating-real-time-recurrent-learning-with-random-kronecker-factors | |
| Contamination Attacks and Mitigation in Multi-Party Machine Learning | Jamie Hayes | https://papers.nips.cc/paper/7895-contamination-attacks-and-mitigation-in-multi-party-machine-learning | |
| An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression | Sheng Chen | https://papers.nips.cc/paper/7896-an-improved-analysis-of-alternating-minimization-for-structured-multi-response-regression | |
| Incorporating Context into Language Encoding Models for fMRI | Shailee Jain | https://papers.nips.cc/paper/7897-incorporating-context-into-language-encoding-models-for-fmri | |
| CatBoost: unbiased boosting with categorical features | Liudmila Prokhorenkova | https://papers.nips.cc/paper/7898-catboost-unbiased-boosting-with-categorical-features | |
| Query K-means Clustering and the Double Dixie Cup Problem | I Chien | https://papers.nips.cc/paper/7899-query-k-means-clustering-and-the-double-dixie-cup-problem | |
| Training Neural Networks Using Features Replay | Zhouyuan Huo | https://papers.nips.cc/paper/7900-training-neural-networks-using-features-replay | |
| Modeling Dynamic Missingness of Implicit Feedback for Recommendation | Menghan Wang | https://papers.nips.cc/paper/7901-modeling-dynamic-missingness-of-implicit-feedback-for-recommendation | |
| Representation Learning of Compositional Data | Marta Avalos | https://papers.nips.cc/paper/7902-representation-learning-of-compositional-data | |
| Model-based targeted dimensionality reduction for neuronal population data | Mikio Aoi | https://papers.nips.cc/paper/7903-model-based-targeted-dimensionality-reduction-for-neuronal-population-data | |
| On gradient regularizers for MMD GANs | Michael Arbel | https://papers.nips.cc/paper/7904-on-gradient-regularizers-for-mmd-gans | |
| Heterogeneous Multi-output Gaussian Process Prediction | Pablo Moreno-Muñoz | https://papers.nips.cc/paper/7905-heterogeneous-multi-output-gaussian-process-prediction | |
| Large-Scale Stochastic Sampling from the Probability Simplex | Jack Baker | https://papers.nips.cc/paper/7906-large-scale-stochastic-sampling-from-the-probability-simplex | |
| Policy Regret in Repeated Games | Raman Arora | https://papers.nips.cc/paper/7907-policy-regret-in-repeated-games | |
| A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice | Hendrik Fichtenberger | https://papers.nips.cc/paper/7908-a-theory-based-evaluation-of-nearest-neighbor-models-put-into-practice | |
| Banach Wasserstein GAN | Jonas Adler | https://papers.nips.cc/paper/7909-banach-wasserstein-gan | |
| Provable Gaussian Embedding with One Observation | Ming Yu | https://papers.nips.cc/paper/7910-provable-gaussian-embedding-with-one-observation | |
| BRITS: Bidirectional Recurrent Imputation for Time Series | Wei Cao | https://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series | |
| M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search | Yelong Shen | https://papers.nips.cc/paper/7912-m-walk-learning-to-walk-over-graphs-using-monte-carlo-tree-search | |
| Extracting Relationships by Multi-Domain Matching | Yitong Li | https://papers.nips.cc/paper/7913-extracting-relationships-by-multi-domain-matching | |
| Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses | Corinna Cortes | https://papers.nips.cc/paper/7914-efficient-gradient-computation-for-structured-output-learning-with-rational-and-tropical-losses | |
| Generative Probabilistic Novelty Detection with Adversarial Autoencoders | Stanislav Pidhorskyi | https://papers.nips.cc/paper/7915-generative-probabilistic-novelty-detection-with-adversarial-autoencoders | |
| Diminishing Returns Shape Constraints for Interpretability and Regularization | Maya Gupta | https://papers.nips.cc/paper/7916-diminishing-returns-shape-constraints-for-interpretability-and-regularization | |
| Scalable Hyperparameter Transfer Learning | Valerio Perrone | https://papers.nips.cc/paper/7917-scalable-hyperparameter-transfer-learning | |
| Stochastic Nonparametric Event-Tensor Decomposition | Shandian Zhe | https://papers.nips.cc/paper/7918-stochastic-nonparametric-event-tensor-decomposition | |
| Scaling Gaussian Process Regression with Derivatives | David Eriksson | https://papers.nips.cc/paper/7919-scaling-gaussian-process-regression-with-derivatives | |
| Differentially Private Testing of Identity and Closeness of Discrete Distributions | Jayadev Acharya | https://papers.nips.cc/paper/7920-differentially-private-testing-of-identity-and-closeness-of-discrete-distributions | |
| Bayesian Adversarial Learning | Nanyang Ye | https://papers.nips.cc/paper/7921-bayesian-adversarial-learning | |
| Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms | Kishan Wimalawarne | https://papers.nips.cc/paper/7922-efficient-convex-completion-of-coupled-tensors-using-coupled-nuclear-norms | |
| Maximizing Induced Cardinality Under a Determinantal Point Process | Jennifer A. Gillenwater | https://papers.nips.cc/paper/7923-maximizing-induced-cardinality-under-a-determinantal-point-process | |
| Causal Inference with Noisy and Missing Covariates via Matrix Factorization | Nathan Kallus | https://papers.nips.cc/paper/7924-causal-inference-with-noisy-and-missing-covariates-via-matrix-factorization | |
| rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions | Mathieu Fehr | https://papers.nips.cc/paper/7925-rho-pomdps-have-lipschitz-continuous-epsilon-optimal-value-functions | |
| Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks | Agastya Kalra | https://papers.nips.cc/paper/7926-online-structure-learning-for-feed-forward-and-recurrent-sum-product-networks | |
| Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss | Stephen Mussmann | https://papers.nips.cc/paper/7927-uncertainty-sampling-is-preconditioned-stochastic-gradient-descent-on-zero-one-loss | |
| A Probabilistic U-Net for Segmentation of Ambiguous Images | Simon Kohl | https://papers.nips.cc/paper/7928-a-probabilistic-u-net-for-segmentation-of-ambiguous-images | |
| Unorganized Malicious Attacks Detection | Ming Pang | https://papers.nips.cc/paper/7929-unorganized-malicious-attacks-detection | |
| Causal Inference via Kernel Deviance Measures | Jovana Mitrovic | https://papers.nips.cc/paper/7930-causal-inference-via-kernel-deviance-measures | |
| Bayesian Alignments of Warped Multi-Output Gaussian Processes | Markus Kaiser | https://papers.nips.cc/paper/7931-bayesian-alignments-of-warped-multi-output-gaussian-processes | |
| Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks | Yingyezhe Jin | https://papers.nips.cc/paper/7932-hybrid-macromicro-level-backpropagation-for-training-deep-spiking-neural-networks | |
| Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation | Kush Bhatia | https://papers.nips.cc/paper/7933-gen-oja-simple-efficient-algorithm-for-streaming-generalized-eigenvector-computation | |
| Efficient online algorithms for fast-rate regret bounds under sparsity | Pierre Gaillard | https://papers.nips.cc/paper/7934-efficient-online-algorithms-for-fast-rate-regret-bounds-under-sparsity | |
| GILBO: One Metric to Measure Them All | Alexander A. Alemi | https://papers.nips.cc/paper/7935-gilbo-one-metric-to-measure-them-all | |
| Predictive Uncertainty Estimation via Prior Networks | Andrey Malinin | https://papers.nips.cc/paper/7936-predictive-uncertainty-estimation-via-prior-networks | |
| Dual Policy Iteration | Wen Sun | https://papers.nips.cc/paper/7937-dual-policy-iteration | |
| A probabilistic population code based on neural samples | Sabyasachi Shivkumar | https://papers.nips.cc/paper/7938-a-probabilistic-population-code-based-on-neural-samples | |
| Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks | Anirvan Sengupta | https://papers.nips.cc/paper/7939-manifold-tiling-localized-receptive-fields-are-optimal-in-similarity-preserving-neural-networks | |
| On the Convergence and Robustness of Training GANs with Regularized Optimal Transport | Maziar Sanjabi | https://papers.nips.cc/paper/7940-on-the-convergence-and-robustness-of-training-gans-with-regularized-optimal-transport | |
| Model-Agnostic Private Learning | Raef Bassily | https://papers.nips.cc/paper/7941-model-agnostic-private-learning | |
| Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders | Tengfei Ma | https://papers.nips.cc/paper/7942-constrained-generation-of-semantically-valid-graphs-via-regularizing-variational-autoencoders | |
| Provably Correct Automatic Sub-Differentiation for Qualified Programs | Sham M. Kakade | https://papers.nips.cc/paper/7943-provably-correct-automatic-sub-differentiation-for-qualified-programs | |
| Deep Homogeneous Mixture Models: Representation, Separation, and Approximation | Priyank Jaini | https://papers.nips.cc/paper/7944-deep-homogeneous-mixture-models-representation-separation-and-approximation | |
| Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks | Grant Rotskoff | https://papers.nips.cc/paper/7945-parameters-as-interacting-particles-long-time-convergence-and-asymptotic-error-scaling-of-neural-networks | |
| Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies | Sungryull Sohn | https://papers.nips.cc/paper/7946-hierarchical-reinforcement-learning-for-zero-shot-generalization-with-subtask-dependencies | |
| A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks | Kimin Lee | https://papers.nips.cc/paper/7947-a-simple-unified-framework-for-detecting-out-of-distribution-samples-and-adversarial-attacks | |
| End-to-End Differentiable Physics for Learning and Control | Filipe de Avila Belbute-Peres | https://papers.nips.cc/paper/7948-end-to-end-differentiable-physics-for-learning-and-control | |
| BRUNO: A Deep Recurrent Model for Exchangeable Data | Iryna Korshunova | https://papers.nips.cc/paper/7949-bruno-a-deep-recurrent-model-for-exchangeable-data | |
| Stimulus domain transfer in recurrent models for large scale cortical population prediction on video | Fabian Sinz | https://papers.nips.cc/paper/7950-stimulus-domain-transfer-in-recurrent-models-for-large-scale-cortical-population-prediction-on-video | |
| Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction | Roei Herzig | https://papers.nips.cc/paper/7951-mapping-images-to-scene-graphs-with-permutation-invariant-structured-prediction | |
| Distributed Multi-Player Bandits - a Game of Thrones Approach | Ilai Bistritz | https://papers.nips.cc/paper/7952-distributed-multi-player-bandits-a-game-of-thrones-approach | |
| Efficient Loss-Based Decoding on Graphs for Extreme Classification | Itay Evron | https://papers.nips.cc/paper/7953-efficient-loss-based-decoding-on-graphs-for-extreme-classification | |
| Chaining Mutual Information and Tightening Generalization Bounds | Amir Asadi | https://papers.nips.cc/paper/7954-chaining-mutual-information-and-tightening-generalization-bounds | |
| Implicit Probabilistic Integrators for ODEs | Onur Teymur | https://papers.nips.cc/paper/7955-implicit-probabilistic-integrators-for-odes | |
| Learning Attentional Communication for Multi-Agent Cooperation | Jiechuan Jiang | https://papers.nips.cc/paper/7956-learning-attentional-communication-for-multi-agent-cooperation | |
| Training Deep Models Faster with Robust, Approximate Importance Sampling | Tyler B. Johnson | https://papers.nips.cc/paper/7957-training-deep-models-faster-with-robust-approximate-importance-sampling | |
| Bandit Learning with Implicit Feedback | Yi Qi | https://papers.nips.cc/paper/7958-bandit-learning-with-implicit-feedback | |
| Unsupervised Text Style Transfer using Language Models as Discriminators | Zichao Yang | https://papers.nips.cc/paper/7959-unsupervised-text-style-transfer-using-language-models-as-discriminators | |
| Relational recurrent neural networks | Adam Santoro | https://papers.nips.cc/paper/7960-relational-recurrent-neural-networks | |
| Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features | Md Enayat Ullah | https://papers.nips.cc/paper/7961-streaming-kernel-pca-with-tildeosqrtn-random-features | |
| REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis | Yu-Shao Peng | https://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis | |
| Bayesian Model-Agnostic Meta-Learning | Jaesik Yoon | https://papers.nips.cc/paper/7963-bayesian-model-agnostic-meta-learning | |
| Disconnected Manifold Learning for Generative Adversarial Networks | Mahyar Khayatkhoei | https://papers.nips.cc/paper/7964-disconnected-manifold-learning-for-generative-adversarial-networks | |
| Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces | Yu-An Chung | https://papers.nips.cc/paper/7965-unsupervised-cross-modal-alignment-of-speech-and-text-embedding-spaces | |
| Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem | Victor-Emmanuel Brunel | https://papers.nips.cc/paper/7966-learning-signed-determinantal-point-processes-through-the-principal-minor-assignment-problem | |
| Out-of-Distribution Detection using Multiple Semantic Label Representations | Gabi Shalev | https://papers.nips.cc/paper/7967-out-of-distribution-detection-using-multiple-semantic-label-representations | |
| Stochastic Chebyshev Gradient Descent for Spectral Optimization | Insu Han | https://papers.nips.cc/paper/7968-stochastic-chebyshev-gradient-descent-for-spectral-optimization | |
| Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation | Sofiane Dhouib | https://papers.nips.cc/paper/7969-revisiting-epsilon-gamma-tau-similarity-learning-for-domain-adaptation | |
| How to tell when a clustering is (approximately) correct using convex relaxations | Marina Meila | https://papers.nips.cc/paper/7970-how-to-tell-when-a-clustering-is-approximately-correct-using-convex-relaxations | |
| Constant Regret, Generalized Mixability, and Mirror Descent | Zakaria Mhammedi | https://papers.nips.cc/paper/7971-constant-regret-generalized-mixability-and-mirror-descent | |
| A Bayesian Approach to Generative Adversarial Imitation Learning | Wonseok Jeon | https://papers.nips.cc/paper/7972-a-bayesian-approach-to-generative-adversarial-imitation-learning | |
| Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis | Alyson K. Fletcher | https://papers.nips.cc/paper/7973-plug-in-estimation-in-high-dimensional-linear-inverse-problems-a-rigorous-analysis | |
| Constrained Cross-Entropy Method for Safe Reinforcement Learning | Min Wen | https://papers.nips.cc/paper/7974-constrained-cross-entropy-method-for-safe-reinforcement-learning | |
| Multi-Agent Generative Adversarial Imitation Learning | Jiaming Song | https://papers.nips.cc/paper/7975-multi-agent-generative-adversarial-imitation-learning | |
| Adaptive Learning with Unknown Information Flows | Yonatan Gur | https://papers.nips.cc/paper/7976-adaptive-learning-with-unknown-information-flows | |
| Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks | Bryan Lim | https://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks | |
| Generative modeling for protein structures | Namrata Anand | https://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures | |
| Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo | Marton Havasi | https://papers.nips.cc/paper/7979-inference-in-deep-gaussian-processes-using-stochastic-gradient-hamiltonian-monte-carlo | |
| Knowledge Distillation by On-the-Fly Native Ensemble | xu lan | https://papers.nips.cc/paper/7980-knowledge-distillation-by-on-the-fly-native-ensemble | |
| Non-Adversarial Mapping with VAEs | Yedid Hoshen | https://papers.nips.cc/paper/7981-non-adversarial-mapping-with-vaes | |
| Generalisation in humans and deep neural networks | Robert Geirhos | https://papers.nips.cc/paper/7982-generalisation-in-humans-and-deep-neural-networks | |
| Towards Text Generation with Adversarially Learned Neural Outlines | Sandeep Subramanian | https://papers.nips.cc/paper/7983-towards-text-generation-with-adversarially-learned-neural-outlines | |
| cpSGD: Communication-efficient and differentially-private distributed SGD | Naman Agarwal | https://papers.nips.cc/paper/7984-cpsgd-communication-efficient-and-differentially-private-distributed-sgd | |
| GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration | Jacob Gardner | https://papers.nips.cc/paper/7985-gpytorch-blackbox-matrix-matrix-gaussian-process-inference-with-gpu-acceleration | |
| Diffusion Maps for Textual Network Embedding | Xinyuan Zhang | https://papers.nips.cc/paper/7986-diffusion-maps-for-textual-network-embedding | |
| Simple, Distributed, and Accelerated Probabilistic Programming | Dustin Tran | https://papers.nips.cc/paper/7987-simple-distributed-and-accelerated-probabilistic-programming | |
| VideoCapsuleNet: A Simplified Network for Action Detection | Kevin Duarte | https://papers.nips.cc/paper/7988-videocapsulenet-a-simplified-network-for-action-detection | |
| Rectangular Bounding Process | Xuhui Fan | https://papers.nips.cc/paper/7989-rectangular-bounding-process | |
| Improved Algorithms for Collaborative PAC Learning | Huy Nguyen | https://papers.nips.cc/paper/7990-improved-algorithms-for-collaborative-pac-learning | |
| Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding | Nan Rosemary Ke | https://papers.nips.cc/paper/7991-sparse-attentive-backtracking-temporal-credit-assignment-through-reminding | |
| Communication Compression for Decentralized Training | Hanlin Tang | https://papers.nips.cc/paper/7992-communication-compression-for-decentralized-training | |
| Depth-Limited Solving for Imperfect-Information Games | Noam Brown | https://papers.nips.cc/paper/7993-depth-limited-solving-for-imperfect-information-games | |
| Training Deep Neural Networks with 8-bit Floating Point Numbers | Naigang Wang | https://papers.nips.cc/paper/7994-training-deep-neural-networks-with-8-bit-floating-point-numbers | |
| Scalar Posterior Sampling with Applications | Georgios Theocharous | https://papers.nips.cc/paper/7995-scalar-posterior-sampling-with-applications | |
| Understanding Batch Normalization | Nils Bjorck | https://papers.nips.cc/paper/7996-understanding-batch-normalization | |
| Adversarial Scene Editing: Automatic Object Removal from Weak Supervision | Rakshith R. Shetty | https://papers.nips.cc/paper/7997-adversarial-scene-editing-automatic-object-removal-from-weak-supervision | |
| Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples | Guanhong Tao | https://papers.nips.cc/paper/7998-attacks-meet-interpretability-attribute-steered-detection-of-adversarial-samples | |
| On Neuronal Capacity | Pierre Baldi | https://papers.nips.cc/paper/7999-on-neuronal-capacity | |
| Breaking the Activation Function Bottleneck through Adaptive Parameterization | Sebastian Flennerhag | https://papers.nips.cc/paper/8000-breaking-the-activation-function-bottleneck-through-adaptive-parameterization | |
| Learning Loop Invariants for Program Verification | Xujie Si | https://papers.nips.cc/paper/8001-learning-loop-invariants-for-program-verification | |
| Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization | Bruno Korbar | https://papers.nips.cc/paper/8002-cooperative-learning-of-audio-and-video-models-from-self-supervised-synchronization | |
| Towards Robust Interpretability with Self-Explaining Neural Networks | David Alvarez Melis | https://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks | |
| Deep State Space Models for Time Series Forecasting | Syama Sundar Rangapuram | https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting | |
| Constrained Graph Variational Autoencoders for Molecule Design | Qi Liu | https://papers.nips.cc/paper/8005-constrained-graph-variational-autoencoders-for-molecule-design | |
| Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction | Kevin Ellis | https://papers.nips.cc/paper/8006-learning-libraries-of-subroutines-for-neurallyguided-bayesian-program-induction | |
| Neural Architecture Optimization | Renqian Luo | https://papers.nips.cc/paper/8007-neural-architecture-optimization | |
| Preference Based Adaptation for Learning Objectives | Yao-Xiang Ding | https://papers.nips.cc/paper/8008-preference-based-adaptation-for-learning-objectives | |
| Distributed k-Clustering for Data with Heavy Noise | Shi Li | https://papers.nips.cc/paper/8009-distributed-k-clustering-for-data-with-heavy-noise | |
| Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo | HOLDEN LEE | https://papers.nips.cc/paper/8010-beyond-log-concavity-provable-guarantees-for-sampling-multi-modal-distributions-using-simulated-tempering-langevin-monte-carlo | |
| A General Method for Amortizing Variational Filtering | Joseph Marino | https://papers.nips.cc/paper/8011-a-general-method-for-amortizing-variational-filtering | |
| A Reduction for Efficient LDA Topic Reconstruction | Matteo Almanza | https://papers.nips.cc/paper/8012-a-reduction-for-efficient-lda-topic-reconstruction | |
| Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data | Dominik Linzner | https://papers.nips.cc/paper/8013-cluster-variational-approximations-for-structure-learning-of-continuous-time-bayesian-networks-from-incomplete-data | |
| RenderNet: A deep convolutional network for differentiable rendering from 3D shapes | Thu H. Nguyen-Phuoc | https://papers.nips.cc/paper/8014-rendernet-a-deep-convolutional-network-for-differentiable-rendering-from-3d-shapes | |
| Robust Hypothesis Testing Using Wasserstein Uncertainty Sets | RUI GAO | https://papers.nips.cc/paper/8015-robust-hypothesis-testing-using-wasserstein-uncertainty-sets | |
| Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks | Zhihao Zheng | https://papers.nips.cc/paper/8016-robust-detection-of-adversarial-attacks-by-modeling-the-intrinsic-properties-of-deep-neural-networks | |
| Monte-Carlo Tree Search for Constrained POMDPs | Jongmin Lee | https://papers.nips.cc/paper/8017-monte-carlo-tree-search-for-constrained-pomdps | |
| Learning to Repair Software Vulnerabilities with Generative Adversarial Networks | Jacob Harer | https://papers.nips.cc/paper/8018-learning-to-repair-software-vulnerabilities-with-generative-adversarial-networks | |
| Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation | Tianyu He | https://papers.nips.cc/paper/8019-layer-wise-coordination-between-encoder-and-decoder-for-neural-machine-translation | |
| Dirichlet belief networks for topic structure learning | He Zhao | https://papers.nips.cc/paper/8020-dirichlet-belief-networks-for-topic-structure-learning | |
| Stochastic Expectation Maximization with Variance Reduction | Jianfei Chen | https://papers.nips.cc/paper/8021-stochastic-expectation-maximization-with-variance-reduction | |
| Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions | Wenruo Bai | https://papers.nips.cc/paper/8022-submodular-maximization-via-gradient-ascent-the-case-of-deep-submodular-functions | |
| The challenge of realistic music generation: modelling raw audio at scale | Sander Dieleman | https://papers.nips.cc/paper/8023-the-challenge-of-realistic-music-generation-modelling-raw-audio-at-scale | |
| Spectral Signatures in Backdoor Attacks | Brandon Tran | https://papers.nips.cc/paper/8024-spectral-signatures-in-backdoor-attacks | |
| Reward learning from human preferences and demonstrations in Atari | Borja Ibarz | https://papers.nips.cc/paper/8025-reward-learning-from-human-preferences-and-demonstrations-in-atari | |
| Approximate Knowledge Compilation by Online Collapsed Importance Sampling | Tal Friedman | https://papers.nips.cc/paper/8026-approximate-knowledge-compilation-by-online-collapsed-importance-sampling | |
| Neural Arithmetic Logic Units | Andrew Trask | https://papers.nips.cc/paper/8027-neural-arithmetic-logic-units | |
| Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training | Youjie Li | https://papers.nips.cc/paper/8028-pipe-sgd-a-decentralized-pipelined-sgd-framework-for-distributed-deep-net-training | |
| Improved Expressivity Through Dendritic Neural Networks | Xundong Wu | https://papers.nips.cc/paper/8029-improved-expressivity-through-dendritic-neural-networks | |
| Efficient Anomaly Detection via Matrix Sketching | Vatsal Sharan | https://papers.nips.cc/paper/8030-efficient-anomaly-detection-via-matrix-sketching | |
| Learning to Specialize with Knowledge Distillation for Visual Question Answering | Jonghwan Mun | https://papers.nips.cc/paper/8031-learning-to-specialize-with-knowledge-distillation-for-visual-question-answering | |
| A Lyapunov-based Approach to Safe Reinforcement Learning | Yinlam Chow | https://papers.nips.cc/paper/8032-a-lyapunov-based-approach-to-safe-reinforcement-learning | |
| Credit Assignment For Collective Multiagent RL With Global Rewards | Duc Thien Nguyen | https://papers.nips.cc/paper/8033-credit-assignment-for-collective-multiagent-rl-with-global-rewards | |
| Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes | Loucas Pillaud-Vivien | https://papers.nips.cc/paper/8034-statistical-optimality-of-stochastic-gradient-descent-on-hard-learning-problems-through-multiple-passes | |
| Does mitigating ML's impact disparity require treatment disparity? | Zachary Lipton | https://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity | |
| Proximal Graphical Event Models | Debarun Bhattacharjya | https://papers.nips.cc/paper/8036-proximal-graphical-event-models | |
| Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments | Mahdi Imani | https://papers.nips.cc/paper/8037-bayesian-control-of-large-mdps-with-unknown-dynamics-in-data-poor-environments | |
| Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data | Yuanzhi Li | https://papers.nips.cc/paper/8038-learning-overparameterized-neural-networks-via-stochastic-gradient-descent-on-structured-data | |
| Hamiltonian Variational Auto-Encoder | Anthony L. Caterini | https://papers.nips.cc/paper/8039-hamiltonian-variational-auto-encoder | |
| Modelling and unsupervised learning of symmetric deformable object categories | James Thewlis | https://papers.nips.cc/paper/8040-modelling-and-unsupervised-learning-of-symmetric-deformable-object-categories | |
| Graphical model inference: Sequential Monte Carlo meets deterministic approximations | Fredrik Lindsten | https://papers.nips.cc/paper/8041-graphical-model-inference-sequential-monte-carlo-meets-deterministic-approximations | |
| Statistical mechanics of low-rank tensor decomposition | Jonathan Kadmon | https://papers.nips.cc/paper/8042-statistical-mechanics-of-low-rank-tensor-decomposition | |
| Variational Bayesian Monte Carlo | Luigi Acerbi | https://papers.nips.cc/paper/8043-variational-bayesian-monte-carlo | |
| Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion | Jacob Buckman | https://papers.nips.cc/paper/8044-sample-efficient-reinforcement-learning-with-stochastic-ensemble-value-expansion | |
| Efficient Online Portfolio with Logarithmic Regret | Haipeng Luo | https://papers.nips.cc/paper/8045-efficient-online-portfolio-with-logarithmic-regret | |
| Algorithms and Theory for Multiple-Source Adaptation | Judy Hoffman | https://papers.nips.cc/paper/8046-algorithms-and-theory-for-multiple-source-adaptation | |
| Online Reciprocal Recommendation with Theoretical Performance Guarantees | Claudio Gentile | https://papers.nips.cc/paper/8047-online-reciprocal-recommendation-with-theoretical-performance-guarantees | |
| The promises and pitfalls of Stochastic Gradient Langevin Dynamics | Nicolas Brosse | https://papers.nips.cc/paper/8048-the-promises-and-pitfalls-of-stochastic-gradient-langevin-dynamics | |
| How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective | Lei Wu | https://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective | |
| Differentiable MPC for End-to-end Planning and Control | Brandon Amos | https://papers.nips.cc/paper/8050-differentiable-mpc-for-end-to-end-planning-and-control | |
| Bilevel learning of the Group Lasso structure | Jordan Frecon | https://papers.nips.cc/paper/8051-bilevel-learning-of-the-group-lasso-structure | |
| Constructing Unrestricted Adversarial Examples with Generative Models | Yang Song | https://papers.nips.cc/paper/8052-constructing-unrestricted-adversarial-examples-with-generative-models | |
| Information-theoretic Limits for Community Detection in Network Models | Chuyang Ke | https://papers.nips.cc/paper/8053-information-theoretic-limits-for-community-detection-in-network-models | |
| Learning Conditioned Graph Structures for Interpretable Visual Question Answering | Will Norcliffe-Brown | https://papers.nips.cc/paper/8054-learning-conditioned-graph-structures-for-interpretable-visual-question-answering | |
| Distributionally Robust Graphical Models | Rizal Fathony | https://papers.nips.cc/paper/8055-distributionally-robust-graphical-models | |
| Transfer Learning with Neural AutoML | Catherine Wong | https://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl | |
| Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity | Conghui Tan | https://papers.nips.cc/paper/8057-stochastic-primal-dual-method-for-empirical-risk-minimization-with-o1-per-iteration-complexity | |
| On preserving non-discrimination when combining expert advice | Avrim Blum | https://papers.nips.cc/paper/8058-on-preserving-non-discrimination-when-combining-expert-advice | |
| Learning to Play With Intrinsically-Motivated, Self-Aware Agents | Nick Haber | https://papers.nips.cc/paper/8059-learning-to-play-with-intrinsically-motivated-self-aware-agents | |
| Scaling provable adversarial defenses | Eric Wong | https://papers.nips.cc/paper/8060-scaling-provable-adversarial-defenses | |
| Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images | Andrei Zanfir | https://papers.nips.cc/paper/8061-deep-network-for-the-integrated-3d-sensing-of-multiple-people-in-natural-images | |
| Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs | Han Shao | https://papers.nips.cc/paper/8062-almost-optimal-algorithms-for-linear-stochastic-bandits-with-heavy-tailed-payoffs | |
| Data-dependent PAC-Bayes priors via differential privacy | Gintare Karolina Dziugaite | https://papers.nips.cc/paper/8063-data-dependent-pac-bayes-priors-via-differential-privacy | |
| Deep Poisson gamma dynamical systems | Dandan Guo | https://papers.nips.cc/paper/8064-deep-poisson-gamma-dynamical-systems | |
| Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds | Kry Lui | https://papers.nips.cc/paper/8065-dimensionality-reduction-has-quantifiable-imperfections-two-geometric-bounds | |
| Teaching Inverse Reinforcement Learners via Features and Demonstrations | Luis Haug | https://papers.nips.cc/paper/8066-teaching-inverse-reinforcement-learners-via-features-and-demonstrations | |
| Wasserstein Distributionally Robust Kalman Filtering | Soroosh Shafieezadeh Abadeh | https://papers.nips.cc/paper/8067-wasserstein-distributionally-robust-kalman-filtering | |
| Generalisation of structural knowledge in the hippocampal-entorhinal system | James Whittington | https://papers.nips.cc/paper/8068-generalisation-of-structural-knowledge-in-the-hippocampal-entorhinal-system | |
| Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization | Blake E. Woodworth | https://papers.nips.cc/paper/8069-graph-oracle-models-lower-bounds-and-gaps-for-parallel-stochastic-optimization | |
| Adversarial Regularizers in Inverse Problems | Sebastian Lunz | https://papers.nips.cc/paper/8070-adversarial-regularizers-in-inverse-problems | |
| Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms | Vincent Cohen-Addad | https://papers.nips.cc/paper/8071-clustering-redemptionbeyond-the-impossibility-of-kleinbergs-axioms | |
| Co-teaching: Robust training of deep neural networks with extremely noisy labels | Bo Han | https://papers.nips.cc/paper/8072-co-teaching-robust-training-of-deep-neural-networks-with-extremely-noisy-labels | |
| Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition | Justin Fu | https://papers.nips.cc/paper/8073-variational-inverse-control-with-events-a-general-framework-for-data-driven-reward-definition | |
| A convex program for bilinear inversion of sparse vectors | Alireza Aghasi | https://papers.nips.cc/paper/8074-a-convex-program-for-bilinear-inversion-of-sparse-vectors | |
| Adversarial Multiple Source Domain Adaptation | Han Zhao | https://papers.nips.cc/paper/8075-adversarial-multiple-source-domain-adaptation | |
| Neural Tangent Kernel: Convergence and Generalization in Neural Networks | Arthur Jacot-Guillarmod | https://papers.nips.cc/paper/8076-neural-tangent-kernel-convergence-and-generalization-in-neural-networks | |
| Contextual Stochastic Block Models | Yash Deshpande | https://papers.nips.cc/paper/8077-contextual-stochastic-block-models | |
| A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks | Jeffrey Chan | https://papers.nips.cc/paper/8078-a-likelihood-free-inference-framework-for-population-genetic-data-using-exchangeable-neural-networks | |
| Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects | Adam Kosiorek | https://papers.nips.cc/paper/8079-sequential-attend-infer-repeat-generative-modelling-of-moving-objects | |
| Randomized Prior Functions for Deep Reinforcement Learning | Ian Osband | https://papers.nips.cc/paper/8080-randomized-prior-functions-for-deep-reinforcement-learning | |
| Compact Representation of Uncertainty in Clustering | Craig Greenberg | https://papers.nips.cc/paper/8081-compact-representation-of-uncertainty-in-clustering | |
| Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity | Fariborz Salehi | https://papers.nips.cc/paper/8082-learning-without-the-phase-regularized-phasemax-achieves-optimal-sample-complexity | |
| Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages | Michelle Yuan | https://papers.nips.cc/paper/8083-multilingual-anchoring-interactive-topic-modeling-and-alignment-across-languages | |
| Estimators for Multivariate Information Measures in General Probability Spaces | Arman Rahimzamani | https://papers.nips.cc/paper/8084-estimators-for-multivariate-information-measures-in-general-probability-spaces | |
| DeepPINK: reproducible feature selection in deep neural networks | Yang Lu | https://papers.nips.cc/paper/8085-deeppink-reproducible-feature-selection-in-deep-neural-networks | |
| HOUDINI: Lifelong Learning as Program Synthesis | Lazar Valkov | https://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis | |
| Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | Liang-Chieh Chen | https://papers.nips.cc/paper/8087-searching-for-efficient-multi-scale-architectures-for-dense-image-prediction | |
| Orthogonally Decoupled Variational Gaussian Processes | Hugh Salimbeni | https://papers.nips.cc/paper/8088-orthogonally-decoupled-variational-gaussian-processes | |
| Dendritic cortical microcircuits approximate the backpropagation algorithm | João Sacramento | https://papers.nips.cc/paper/8089-dendritic-cortical-microcircuits-approximate-the-backpropagation-algorithm | |
| Learning Plannable Representations with Causal InfoGAN | Thanard Kurutach | https://papers.nips.cc/paper/8090-learning-plannable-representations-with-causal-infogan | |
| Uniform Convergence of Gradients for Non-Convex Learning and Optimization | Dylan J. Foster | https://papers.nips.cc/paper/8091-uniform-convergence-of-gradients-for-non-convex-learning-and-optimization | |
| Automatic differentiation in ML: Where we are and where we should be going | Bart van Merrienboer | https://papers.nips.cc/paper/8092-automatic-differentiation-in-ml-where-we-are-and-where-we-should-be-going | |
| A Bayesian Nonparametric View on Count-Min Sketch | Diana Cai | https://papers.nips.cc/paper/8093-a-bayesian-nonparametric-view-on-count-min-sketch | |
| Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels | Zhilu Zhang | https://papers.nips.cc/paper/8094-generalized-cross-entropy-loss-for-training-deep-neural-networks-with-noisy-labels | |
| Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs | Timur Garipov | https://papers.nips.cc/paper/8095-loss-surfaces-mode-connectivity-and-fast-ensembling-of-dnns | |
| Flexible neural representation for physics prediction | Damian Mrowca | https://papers.nips.cc/paper/8096-flexible-neural-representation-for-physics-prediction | |
| Legendre Decomposition for Tensors | Mahito Sugiyama | https://papers.nips.cc/paper/8097-legendre-decomposition-for-tensors | |
| Reinforcement Learning of Theorem Proving | Cezary Kaliszyk | https://papers.nips.cc/paper/8098-reinforcement-learning-of-theorem-proving | |
| Data Amplification: A Unified and Competitive Approach to Property Estimation | Yi HAO | https://papers.nips.cc/paper/8099-data-amplification-a-unified-and-competitive-approach-to-property-estimation | |
| Group Equivariant Capsule Networks | Jan Eric Lenssen | https://papers.nips.cc/paper/8100-group-equivariant-capsule-networks | |
| Stein Variational Gradient Descent as Moment Matching | Qiang Liu | https://papers.nips.cc/paper/8101-stein-variational-gradient-descent-as-moment-matching | |
| Differential Privacy for Growing Databases | Rachel Cummings | https://papers.nips.cc/paper/8102-differential-privacy-for-growing-databases | |
| Exploration in Structured Reinforcement Learning | Jungseul Ok | https://papers.nips.cc/paper/8103-exploration-in-structured-reinforcement-learning | |
| A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices | Rudrasis Chakraborty | https://papers.nips.cc/paper/8104-a-statistical-recurrent-model-on-the-manifold-of-symmetric-positive-definite-matrices | |
| Balanced Policy Evaluation and Learning | Nathan Kallus | https://papers.nips.cc/paper/8105-balanced-policy-evaluation-and-learning | |
| Distributed Multitask Reinforcement Learning with Quadratic Convergence | Rasul Tutunov | https://papers.nips.cc/paper/8106-distributed-multitask-reinforcement-learning-with-quadratic-convergence | |
| Improving Neural Program Synthesis with Inferred Execution Traces | Richard Shin | https://papers.nips.cc/paper/8107-improving-neural-program-synthesis-with-inferred-execution-traces | |
| Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems | Jung-Su Ha | https://papers.nips.cc/paper/8108-adaptive-path-integral-autoencoders-representation-learning-and-planning-for-dynamical-systems | |
| Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes | Andrea Tirinzoni | https://papers.nips.cc/paper/8109-policy-conditioned-uncertainty-sets-for-robust-markov-decision-processes | |
| GLoMo: Unsupervised Learning of Transferable Relational Graphs | Zhilin Yang | https://papers.nips.cc/paper/8110-glomo-unsupervised-learning-of-transferable-relational-graphs | |
| Online Learning of Quantum States | Scott Aaronson | https://papers.nips.cc/paper/8111-online-learning-of-quantum-states | |
| Wavelet regression and additive models for irregularly spaced data | Asad Haris | https://papers.nips.cc/paper/8112-wavelet-regression-and-additive-models-for-irregularly-spaced-data | |
| Inferring Latent Velocities from Weather Radar Data using Gaussian Processes | Rico Angell | https://papers.nips.cc/paper/8113-inferring-latent-velocities-from-weather-radar-data-using-gaussian-processes | |
| A Structured Prediction Approach for Label Ranking | Anna Korba | https://papers.nips.cc/paper/8114-a-structured-prediction-approach-for-label-ranking | |
| Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | Mojmir Mutny | https://papers.nips.cc/paper/8115-efficient-high-dimensional-bayesian-optimization-with-additivity-and-quadrature-fourier-features | |
| FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network | Aditya Kusupati | https://papers.nips.cc/paper/8116-fastgrnn-a-fast-accurate-stable-and-tiny-kilobyte-sized-gated-recurrent-neural-network | |
| Reversible Recurrent Neural Networks | Matthew MacKay | https://papers.nips.cc/paper/8117-reversible-recurrent-neural-networks | |
| SING: Symbol-to-Instrument Neural Generator | Alexandre Defossez | https://papers.nips.cc/paper/8118-sing-symbol-to-instrument-neural-generator | |
| Learning Compressed Transforms with Low Displacement Rank | Anna Thomas | https://papers.nips.cc/paper/8119-learning-compressed-transforms-with-low-displacement-rank | |
| Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds | Xiaohan Chen | https://papers.nips.cc/paper/8120-theoretical-linear-convergence-of-unfolded-ista-and-its-practical-weights-and-thresholds | |
| Iterative Value-Aware Model Learning | Amir-massoud Farahmand | https://papers.nips.cc/paper/8121-iterative-value-aware-model-learning | |
| Invariant Representations without Adversarial Training | Daniel Moyer | https://papers.nips.cc/paper/8122-invariant-representations-without-adversarial-training | |
| Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias | Abhinav Gupta | https://papers.nips.cc/paper/8123-robot-learning-in-homes-improving-generalization-and-reducing-dataset-bias | |
| Learning Safe Policies with Expert Guidance | Jessie Huang | https://papers.nips.cc/paper/8124-learning-safe-policies-with-expert-guidance | |
| Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data | Ehsan Hajiramezanali | https://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data | |
| Learning SMaLL Predictors | Vikas Garg | https://papers.nips.cc/paper/8126-learning-small-predictors | |
| Phase Retrieval Under a Generative Prior | Paul Hand | https://papers.nips.cc/paper/8127-phase-retrieval-under-a-generative-prior | |
| Quadrature-based features for kernel approximation | Marina Munkhoeva | https://papers.nips.cc/paper/8128-quadrature-based-features-for-kernel-approximation | |
| Reducing Network Agnostophobia | Akshay Raj Dhamija | https://papers.nips.cc/paper/8129-reducing-network-agnostophobia | |
| A Stein variational Newton method | Gianluca Detommaso | https://papers.nips.cc/paper/8130-a-stein-variational-newton-method | |
| Watch Your Step: Learning Node Embeddings via Graph Attention | Sami Abu-El-Haija | https://papers.nips.cc/paper/8131-watch-your-step-learning-node-embeddings-via-graph-attention | |
| Visual Reinforcement Learning with Imagined Goals | Ashvin V. Nair | https://papers.nips.cc/paper/8132-visual-reinforcement-learning-with-imagined-goals | |
| Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition | Kuan Han | https://papers.nips.cc/paper/8133-deep-predictive-coding-network-with-local-recurrent-processing-for-object-recognition | |
| PAC-Bayes bounds for stable algorithms with instance-dependent priors | Omar Rivasplata | https://papers.nips.cc/paper/8134-pac-bayes-bounds-for-stable-algorithms-with-instance-dependent-priors | |
| Beyond Grids: Learning Graph Representations for Visual Recognition | Yin Li | https://papers.nips.cc/paper/8135-beyond-grids-learning-graph-representations-for-visual-recognition | |
| The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization | Constantinos Daskalakis | https://papers.nips.cc/paper/8136-the-limit-points-of-optimistic-gradient-descent-in-min-max-optimization | |
| Coordinate Descent with Bandit Sampling | Farnood Salehi | https://papers.nips.cc/paper/8137-coordinate-descent-with-bandit-sampling | |
| Deep Dynamical Modeling and Control of Unsteady Fluid Flows | Jeremy Morton | https://papers.nips.cc/paper/8138-deep-dynamical-modeling-and-control-of-unsteady-fluid-flows | |
| Confounding-Robust Policy Improvement | Nathan Kallus | https://papers.nips.cc/paper/8139-confounding-robust-policy-improvement | |
| The Importance of Sampling inMeta-Reinforcement Learning | Bradly Stadie | https://papers.nips.cc/paper/8140-the-importance-of-sampling-inmeta-reinforcement-learning | |
| Representer Point Selection for Explaining Deep Neural Networks | Chih-Kuan Yeh | https://papers.nips.cc/paper/8141-representer-point-selection-for-explaining-deep-neural-networks | |
| The Effect of Network Width on the Performance of Large-batch Training | Lingjiao Chen | https://papers.nips.cc/paper/8142-the-effect-of-network-width-on-the-performance-of-large-batch-training | |
| SNIPER: Efficient Multi-Scale Training | Bharat Singh | https://papers.nips.cc/paper/8143-sniper-efficient-multi-scale-training | |
| The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models | Chen Dan | https://papers.nips.cc/paper/8144-the-sample-complexity-of-semi-supervised-learning-with-nonparametric-mixture-models | |
| Hardware Conditioned Policies for Multi-Robot Transfer Learning | Tao Chen | https://papers.nips.cc/paper/8145-hardware-conditioned-policies-for-multi-robot-transfer-learning | |
| Co-regularized Alignment for Unsupervised Domain Adaptation | Abhishek Kumar | https://papers.nips.cc/paper/8146-co-regularized-alignment-for-unsupervised-domain-adaptation | |
| Statistical and Computational Trade-Offs in Kernel K-Means | Daniele Calandriello | https://papers.nips.cc/paper/8147-statistical-and-computational-trade-offs-in-kernel-k-means | |
| Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures | Sergey Bartunov | https://papers.nips.cc/paper/8148-assessing-the-scalability-of-biologically-motivated-deep-learning-algorithms-and-architectures | |
| Learning Attractor Dynamics for Generative Memory | Yan Wu | https://papers.nips.cc/paper/8149-learning-attractor-dynamics-for-generative-memory | |
| The emergence of multiple retinal cell types through efficient coding of natural movies | Samuel Ocko | https://papers.nips.cc/paper/8150-the-emergence-of-multiple-retinal-cell-types-through-efficient-coding-of-natural-movies | |
| Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks | Jie Hu | https://papers.nips.cc/paper/8151-gather-excite-exploiting-feature-context-in-convolutional-neural-networks | |
| The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation | Zi Yin | https://papers.nips.cc/paper/8152-the-global-anchor-method-for-quantifying-linguistic-shifts-and-domain-adaptation | |
| Identification and Estimation of Causal Effects from Dependent Data | Eli Sherman | https://papers.nips.cc/paper/8153-identification-and-estimation-of-causal-effects-from-dependent-data | |
| Deepcode: Feedback Codes via Deep Learning | Hyeji Kim | https://papers.nips.cc/paper/8154-deepcode-feedback-codes-via-deep-learning | |
| Learning and Testing Causal Models with Interventions | Jayadev Acharya | https://papers.nips.cc/paper/8155-learning-and-testing-causal-models-with-interventions | |
| Implicit Bias of Gradient Descent on Linear Convolutional Networks | Suriya Gunasekar | https://papers.nips.cc/paper/8156-implicit-bias-of-gradient-descent-on-linear-convolutional-networks | |
| DAGs with NO TEARS: Continuous Optimization for Structure Learning | Xun Zheng | https://papers.nips.cc/paper/8157-dags-with-no-tears-continuous-optimization-for-structure-learning | |
| PAC-Bayes Tree: weighted subtrees with guarantees | Tin D. Nguyen | https://papers.nips.cc/paper/8158-pac-bayes-tree-weighted-subtrees-with-guarantees | |
| Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint | Rajan Udwani | https://papers.nips.cc/paper/8159-multi-objective-maximization-of-monotone-submodular-functions-with-cardinality-constraint | |
| Sanity Checks for Saliency Maps | Julius Adebayo | https://papers.nips.cc/paper/8160-sanity-checks-for-saliency-maps | |
| Probabilistic Model-Agnostic Meta-Learning | Chelsea Finn | https://papers.nips.cc/paper/8161-probabilistic-model-agnostic-meta-learning | |
| Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach | Michael Gimelfarb | https://papers.nips.cc/paper/8162-reinforcement-learning-with-multiple-experts-a-bayesian-model-combination-approach | |
| e-SNLI: Natural Language Inference with Natural Language Explanations | Oana-Maria Camburu | https://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations | |
| Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis | Thomas George | https://papers.nips.cc/paper/8164-fast-approximate-natural-gradient-descent-in-a-kronecker-factored-eigenbasis | |
| Learning convex bounds for linear quadratic control policy synthesis | Jack Umenberger | https://papers.nips.cc/paper/8165-learning-convex-bounds-for-linear-quadratic-control-policy-synthesis | |
| Neural Proximal Gradient Descent for Compressive Imaging | Morteza Mardani | https://papers.nips.cc/paper/8166-neural-proximal-gradient-descent-for-compressive-imaging | |
| Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation | Liwei Wang | https://papers.nips.cc/paper/8167-towards-understanding-learning-representations-to-what-extent-do-different-neural-networks-learn-the-same-representation | |
| Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization | Rad Niazadeh | https://papers.nips.cc/paper/8168-optimal-algorithms-for-continuous-non-monotone-submodular-and-dr-submodular-maximization | |
| An intriguing failing of convolutional neural networks and the CoordConv solution | Rosanne Liu | https://papers.nips.cc/paper/8169-an-intriguing-failing-of-convolutional-neural-networks-and-the-coordconv-solution | |
| Trading robust representations for sample complexity through self-supervised visual experience | Andrea Tacchetti | https://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience | |
| Invertibility of Convolutional Generative Networks from Partial Measurements | Fangchang Ma | https://papers.nips.cc/paper/8171-invertibility-of-convolutional-generative-networks-from-partial-measurements | |
| Ex ante coordination and collusion in zero-sum multi-player extensive-form games | Gabriele Farina | https://papers.nips.cc/paper/8172-ex-ante-coordination-and-collusion-in-zero-sum-multi-player-extensive-form-games | |
| Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization | Hoi-To Wai | https://papers.nips.cc/paper/8173-multi-agent-reinforcement-learning-via-double-averaging-primal-dual-optimization | |
| Improving Online Algorithms via ML Predictions | Manish Purohit | https://papers.nips.cc/paper/8174-improving-online-algorithms-via-ml-predictions | |
| Global Non-convex Optimization with Discretized Diffusions | Murat A. Erdogdu | https://papers.nips.cc/paper/8175-global-non-convex-optimization-with-discretized-diffusions | |
| Theoretical guarantees for EM under misspecified Gaussian mixture models | Raaz Dwivedi | https://papers.nips.cc/paper/8176-theoretical-guarantees-for-em-under-misspecified-gaussian-mixture-models | |
| Coupled Variational Bayes via Optimization Embedding | Bo Dai | https://papers.nips.cc/paper/8177-coupled-variational-bayes-via-optimization-embedding | |
| Improving Explorability in Variational Inference with Annealed Variational Objectives | Chin-Wei Huang | https://papers.nips.cc/paper/8178-improving-explorability-in-variational-inference-with-annealed-variational-objectives | |
| Latent Alignment and Variational Attention | Yuntian Deng | https://papers.nips.cc/paper/8179-latent-alignment-and-variational-attention | |
| Towards Deep Conversational Recommendations | Raymond Li | https://papers.nips.cc/paper/8180-towards-deep-conversational-recommendations | |
| Unsupervised Depth Estimation, 3D Face Rotation and Replacement | Joel Ruben Antony Moniz | https://papers.nips.cc/paper/8181-unsupervised-depth-estimation-3d-face-rotation-and-replacement | |
| Generalization Bounds for Uniformly Stable Algorithms | Vitaly Feldman | https://papers.nips.cc/paper/8182-generalization-bounds-for-uniformly-stable-algorithms | |
| Deep Anomaly Detection Using Geometric Transformations | Izhak Golan | https://papers.nips.cc/paper/8183-deep-anomaly-detection-using-geometric-transformations | |
| Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport | Theo Lacombe | https://papers.nips.cc/paper/8184-large-scale-computation-of-means-and-clusters-for-persistence-diagrams-using-optimal-transport | |
| Entropy Rate Estimation for Markov Chains with Large State Space | Yanjun Han | https://papers.nips.cc/paper/8185-entropy-rate-estimation-for-markov-chains-with-large-state-space | |
| Adaptive Methods for Nonconvex Optimization | Manzil Zaheer | https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization | |
| Object-Oriented Dynamics Predictor | Guangxiang Zhu | https://papers.nips.cc/paper/8187-object-oriented-dynamics-predictor | |
| Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models | Alexander Neitz | https://papers.nips.cc/paper/8188-adaptive-skip-intervals-temporal-abstraction-for-recurrent-dynamical-models | |
| Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation | Matthew O'Kelly | https://papers.nips.cc/paper/8189-scalable-end-to-end-autonomous-vehicle-testing-via-rare-event-simulation | |
| Reinforcement Learning for Solving the Vehicle Routing Problem | MohammadReza Nazari | https://papers.nips.cc/paper/8190-reinforcement-learning-for-solving-the-vehicle-routing-problem | |
| ATOMO: Communication-efficient Learning via Atomic Sparsification | Hongyi Wang | https://papers.nips.cc/paper/8191-atomo-communication-efficient-learning-via-atomic-sparsification | |
| Dynamic Network Model from Partial Observations | Elahe Ghalebi | https://papers.nips.cc/paper/8192-dynamic-network-model-from-partial-observations | |
| Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies | Alessandro Achille | https://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies | |
| Maximizing acquisition functions for Bayesian optimization | James Wilson | https://papers.nips.cc/paper/8194-maximizing-acquisition-functions-for-bayesian-optimization | |
| On Markov Chain Gradient Descent | Tao Sun | https://papers.nips.cc/paper/8195-on-markov-chain-gradient-descent | |
| Variance-Reduced Stochastic Gradient Descent on Streaming Data | Ellango Jothimurugesan | https://papers.nips.cc/paper/8196-variance-reduced-stochastic-gradient-descent-on-streaming-data | |
| Online Robust Policy Learning in the Presence of Unknown Adversaries | Aaron Havens | https://papers.nips.cc/paper/8197-online-robust-policy-learning-in-the-presence-of-unknown-adversaries | |
| Uplift Modeling from Separate Labels | Ikko Yamane | https://papers.nips.cc/paper/8198-uplift-modeling-from-separate-labels | |
| Learning Invariances using the Marginal Likelihood | Mark van der Wilk | https://papers.nips.cc/paper/8199-learning-invariances-using-the-marginal-likelihood | |
| Non-delusional Q-learning and value-iteration | Tyler Lu | https://papers.nips.cc/paper/8200-non-delusional-q-learning-and-value-iteration | |
| Using Large Ensembles of Control Variates for Variational Inference | Tomas Geffner | https://papers.nips.cc/paper/8201-using-large-ensembles-of-control-variates-for-variational-inference | |
| Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization | Yuanxiang Gao | https://papers.nips.cc/paper/8202-post-device-placement-with-cross-entropy-minimization-and-proximal-policy-optimization | |
| Learning to Reason with Third Order Tensor Products | Imanol Schlag | https://papers.nips.cc/paper/8203-learning-to-reason-with-third-order-tensor-products | |
| Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing | Chen Liang | https://papers.nips.cc/paper/8204-memory-augmented-policy-optimization-for-program-synthesis-and-semantic-parsing | |
| Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams | Tam Le | https://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams | |
| Neural Voice Cloning with a Few Samples | Sercan Arik | https://papers.nips.cc/paper/8206-neural-voice-cloning-with-a-few-samples | |
| Blind Deconvolutional Phase Retrieval via Convex Programming | Ali Ahmed | https://papers.nips.cc/paper/8207-blind-deconvolutional-phase-retrieval-via-convex-programming | |
| Scalable Laplacian K-modes | Imtiaz Ziko | https://papers.nips.cc/paper/8208-scalable-laplacian-k-modes | |
| A Retrieve-and-Edit Framework for Predicting Structured Outputs | Tatsunori B. Hashimoto | https://papers.nips.cc/paper/8209-a-retrieve-and-edit-framework-for-predicting-structured-outputs | |
| Testing for Families of Distributions via the Fourier Transform | Alistair Stewart | https://papers.nips.cc/paper/8210-testing-for-families-of-distributions-via-the-fourier-transform | |
| Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform | Mitali Bafna | https://papers.nips.cc/paper/8211-thwarting-adversarial-examples-an-l_0-robust-sparse-fourier-transform | |
| Blockwise Parallel Decoding for Deep Autoregressive Models | Mitchell Stern | https://papers.nips.cc/paper/8212-blockwise-parallel-decoding-for-deep-autoregressive-models | |
| Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch | Osman Asif Malik | https://papers.nips.cc/paper/8213-low-rank-tucker-decomposition-of-large-tensors-using-tensorsketch | |
| A Simple Cache Model for Image Recognition | Emin Orhan | https://papers.nips.cc/paper/8214-a-simple-cache-model-for-image-recognition | |
| Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network | Risi Kondor | https://papers.nips.cc/paper/8215-clebschgordan-nets-a-fully-fourier-space-spherical-convolutional-neural-network | |
| Bayesian Nonparametric Spectral Estimation | Felipe Tobar | https://papers.nips.cc/paper/8216-bayesian-nonparametric-spectral-estimation | |
| A Spectral View of Adversarially Robust Features | Shivam Garg | https://papers.nips.cc/paper/8217-a-spectral-view-of-adversarially-robust-features | |
| Synaptic Strength For Convolutional Neural Network | CHEN LIN | https://papers.nips.cc/paper/8218-synaptic-strength-for-convolutional-neural-network | |
| Human-in-the-Loop Interpretability Prior | Isaac Lage | https://papers.nips.cc/paper/8219-human-in-the-loop-interpretability-prior | |
| Learning To Learn Around A Common Mean | Giulia Denevi | https://papers.nips.cc/paper/8220-learning-to-learn-around-a-common-mean | |
| Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming | Fei Wang | https://papers.nips.cc/paper/8221-backpropagation-with-callbacks-foundations-for-efficient-and-expressive-differentiable-programming | |
| Learning with SGD and Random Features | Luigi Carratino | https://papers.nips.cc/paper/8222-learning-with-sgd-and-random-features | |
| Total stochastic gradient algorithms and applications in reinforcement learning | Paavo Parmas | https://papers.nips.cc/paper/8223-total-stochastic-gradient-algorithms-and-applications-in-reinforcement-learning | |
| Glow: Generative Flow with Invertible 1x1 Convolutions | Durk P. Kingma | https://papers.nips.cc/paper/8224-glow-generative-flow-with-invertible-1x1-convolutions | |
| Nonparametric Density Estimation under Adversarial Losses | Shashank Singh | https://papers.nips.cc/paper/8225-nonparametric-density-estimation-under-adversarial-losses | |
| Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions | Boris Muzellec | https://papers.nips.cc/paper/8226-generalizing-point-embeddings-using-the-wasserstein-space-of-elliptical-distributions | |
| Learning to Share and Hide Intentions using Information Regularization | Daniel Strouse | https://papers.nips.cc/paper/8227-learning-to-share-and-hide-intentions-using-information-regularization | |
| Predictive Approximate Bayesian Computation via Saddle Points | Yingxiang Yang | https://papers.nips.cc/paper/8228-predictive-approximate-bayesian-computation-via-saddle-points | |
| Robustness of conditional GANs to noisy labels | Kiran K. Thekumparampil | https://papers.nips.cc/paper/8229-robustness-of-conditional-gans-to-noisy-labels | |
| Robust Learning of Fixed-Structure Bayesian Networks | Yu Cheng | https://papers.nips.cc/paper/8230-robust-learning-of-fixed-structure-bayesian-networks | |
| Improving Simple Models with Confidence Profiles | Amit Dhurandhar | https://papers.nips.cc/paper/8231-improving-simple-models-with-confidence-profiles | |
| PCA of high dimensional random walks with comparison to neural network training | Joseph Antognini | https://papers.nips.cc/paper/8232-pca-of-high-dimensional-random-walks-with-comparison-to-neural-network-training | |
| Learning to Solve SMT Formulas | Mislav Balunovic | https://papers.nips.cc/paper/8233-learning-to-solve-smt-formulas | |
| Lifted Weighted Mini-Bucket | Nicholas Gallo | https://papers.nips.cc/paper/8234-lifted-weighted-mini-bucket | |
| Learning and Inference in Hilbert Space with Quantum Graphical Models | Siddarth Srinivasan | https://papers.nips.cc/paper/8235-learning-and-inference-in-hilbert-space-with-quantum-graphical-models | |
| Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound | Hadi Kazemi | https://papers.nips.cc/paper/8236-unsupervised-image-to-image-translation-using-domain-specific-variational-information-bound | |
| Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution | Dimitrios Diochnos | https://papers.nips.cc/paper/8237-adversarial-risk-and-robustness-general-definitions-and-implications-for-the-uniform-distribution | |
| Gaussian Process Prior Variational Autoencoders | Francesco Paolo Casale | https://papers.nips.cc/paper/8238-gaussian-process-prior-variational-autoencoders | |
| 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data | Maurice Weiler | https://papers.nips.cc/paper/8239-3d-steerable-cnns-learning-rotationally-equivariant-features-in-volumetric-data | |
| Context-aware Synthesis and Placement of Object Instances | Donghoon Lee | https://papers.nips.cc/paper/8240-context-aware-synthesis-and-placement-of-object-instances | |
| Convex Elicitation of Continuous Properties | Jessica Finocchiaro | https://papers.nips.cc/paper/8241-convex-elicitation-of-continuous-properties | |
| Mesh-TensorFlow: Deep Learning for Supercomputers | Noam Shazeer | https://papers.nips.cc/paper/8242-mesh-tensorflow-deep-learning-for-supercomputers | |
| Learning Abstract Options | Matthew Riemer | https://papers.nips.cc/paper/8243-learning-abstract-options | |
| Bounded-Loss Private Prediction Markets | Rafael Frongillo | https://papers.nips.cc/paper/8244-bounded-loss-private-prediction-markets | |
| Temporal alignment and latent Gaussian process factor inference in population spike trains | Lea Duncker | https://papers.nips.cc/paper/8245-temporal-alignment-and-latent-gaussian-process-factor-inference-in-population-spike-trains | |
| Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise | Dan Hendrycks | https://papers.nips.cc/paper/8246-using-trusted-data-to-train-deep-networks-on-labels-corrupted-by-severe-noise | |
| Discretely Relaxing Continuous Variables for tractable Variational Inference | Trefor Evans | https://papers.nips.cc/paper/8247-discretely-relaxing-continuous-variables-for-tractable-variational-inference | |
| Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior | Zi Wang | https://papers.nips.cc/paper/8248-regret-bounds-for-meta-bayesian-optimization-with-an-unknown-gaussian-process-prior | |
| Diversity-Driven Exploration Strategy for Deep Reinforcement Learning | Zhang-Wei Hong | https://papers.nips.cc/paper/8249-diversity-driven-exploration-strategy-for-deep-reinforcement-learning | |
| Deep Generative Models with Learnable Knowledge Constraints | Zhiting Hu | https://papers.nips.cc/paper/8250-deep-generative-models-with-learnable-knowledge-constraints | |
| The Sparse Manifold Transform | Yubei Chen | https://papers.nips.cc/paper/8251-the-sparse-manifold-transform | |
| Bayesian Structure Learning by Recursive Bootstrap | Raanan Y. Rohekar | https://papers.nips.cc/paper/8252-bayesian-structure-learning-by-recursive-bootstrap | |
| Complex Gated Recurrent Neural Networks | Moritz Wolter | https://papers.nips.cc/paper/8253-complex-gated-recurrent-neural-networks | |
| Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders | Abubakar Abid | https://papers.nips.cc/paper/8254-learning-a-warping-distance-from-unlabeled-time-series-using-sequence-autoencoders | |
| Streamlining Variational Inference for Constraint Satisfaction Problems | Aditya Grover | https://papers.nips.cc/paper/8255-streamlining-variational-inference-for-constraint-satisfaction-problems | |
| Fast deep reinforcement learning using online adjustments from the past | Steven Hansen | https://papers.nips.cc/paper/8256-fast-deep-reinforcement-learning-using-online-adjustments-from-the-past | |
| Improved Network Robustness with Adversary Critic | Alexander Matyasko | https://papers.nips.cc/paper/8257-improved-network-robustness-with-adversary-critic | |
| Regret Bounds for Online Portfolio Selection with a Cardinality Constraint | Shinji Ito | https://papers.nips.cc/paper/8258-regret-bounds-for-online-portfolio-selection-with-a-cardinality-constraint | |
| Sketching Method for Large Scale Combinatorial Inference | Wei Sun | https://papers.nips.cc/paper/8259-sketching-method-for-large-scale-combinatorial-inference | |
| Connecting Optimization and Regularization Paths | Arun Suggala | https://papers.nips.cc/paper/8260-connecting-optimization-and-regularization-paths | |
| Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices | Jinhwan Park | https://papers.nips.cc/paper/8261-fully-neural-network-based-speech-recognition-on-mobile-and-embedded-devices | |
| Understanding Regularized Spectral Clustering via Graph Conductance | Yilin Zhang | https://papers.nips.cc/paper/8262-understanding-regularized-spectral-clustering-via-graph-conductance | |
| Data-Driven Clustering via Parameterized Lloyd's Families | Maria-Florina F. Balcan | https://papers.nips.cc/paper/8263-data-driven-clustering-via-parameterized-lloyds-families | |
| Learning Beam Search Policies via Imitation Learning | Renato Negrinho | https://papers.nips.cc/paper/8264-learning-beam-search-policies-via-imitation-learning | |
| Benefits of over-parameterization with EM | Ji Xu | https://papers.nips.cc/paper/8265-benefits-of-over-parameterization-with-em | |
| Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning | Rui Luo | https://papers.nips.cc/paper/8266-thermostat-assisted-continuously-tempered-hamiltonian-monte-carlo-for-bayesian-learning | |
| Robust Subspace Approximation in a Stream | Roie Levin | https://papers.nips.cc/paper/8267-robust-subspace-approximation-in-a-stream | |
| Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues | Soumendu Sundar Mukherjee | https://papers.nips.cc/paper/8268-mean-field-for-the-stochastic-blockmodel-optimization-landscape-and-convergence-issues | |
| Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems | Yair Carmon | https://papers.nips.cc/paper/8269-analysis-of-krylov-subspace-solutions-of-regularized-non-convex-quadratic-problems | |
| Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language | Matthew D. Hoffman | https://papers.nips.cc/paper/8270-autoconj-recognizing-and-exploiting-conjugacy-without-a-domain-specific-language | |
| DropBlock: A regularization method for convolutional networks | Golnaz Ghiasi | https://papers.nips.cc/paper/8271-dropblock-a-regularization-method-for-convolutional-networks | |
| Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger | Gabriel Synnaeve | https://papers.nips.cc/paper/8272-forward-modeling-for-partial-observation-strategy-games-a-starcraft-defogger | |
| With Friends Like These, Who Needs Adversaries? | Saumya Jetley | https://papers.nips.cc/paper/8273-with-friends-like-these-who-needs-adversaries | |
| Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters | Pavel Dvurechenskii | https://papers.nips.cc/paper/8274-decentralize-and-randomize-faster-algorithm-for-wasserstein-barycenters | |
| Joint Autoregressive and Hierarchical Priors for Learned Image Compression | David Minnen | https://papers.nips.cc/paper/8275-joint-autoregressive-and-hierarchical-priors-for-learned-image-compression | |
| Learning Temporal Point Processes via Reinforcement Learning | Shuang Li | https://papers.nips.cc/paper/8276-learning-temporal-point-processes-via-reinforcement-learning | |
| Bias and Generalization in Deep Generative Models: An Empirical Study | Shengjia Zhao | https://papers.nips.cc/paper/8277-bias-and-generalization-in-deep-generative-models-an-empirical-study | |
| Fast and Effective Robustness Certification | Gagandeep Singh | https://papers.nips.cc/paper/8278-fast-and-effective-robustness-certification | |
| Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds | Raghav Somani | https://papers.nips.cc/paper/8279-support-recovery-for-orthogonal-matching-pursuit-upper-and-lower-bounds | |
| Differentially Private Change-Point Detection | Rachel Cummings | https://papers.nips.cc/paper/8280-differentially-private-change-point-detection | |
| Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations | Tong Wang | https://papers.nips.cc/paper/8281-multi-value-rule-sets-for-interpretable-classification-with-feature-efficient-representations | |
| Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions | Sara Magliacane | https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions | |
| Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons | Nima Anari | https://papers.nips.cc/paper/8283-smoothed-analysis-of-discrete-tensor-decomposition-and-assemblies-of-neurons | |
| MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization | Ian En-Hsu Yen | https://papers.nips.cc/paper/8284-mixlasso-generalized-mixed-regression-via-convex-atomic-norm-regularization | |
| Semidefinite relaxations for certifying robustness to adversarial examples | Aditi Raghunathan | https://papers.nips.cc/paper/8285-semidefinite-relaxations-for-certifying-robustness-to-adversarial-examples | |
| Removing Hidden Confounding by Experimental Grounding | Nathan Kallus | https://papers.nips.cc/paper/8286-removing-hidden-confounding-by-experimental-grounding | |
| Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements | Ankush Mandal | https://papers.nips.cc/paper/8287-topkapi-parallel-and-fast-sketches-for-finding-top-k-frequent-elements | |
| Contrastive Learning from Pairwise Measurements | Yi Chen | https://papers.nips.cc/paper/8288-contrastive-learning-from-pairwise-measurements | |
| Point process latent variable models of larval zebrafish behavior | Anuj Sharma | https://papers.nips.cc/paper/8289-point-process-latent-variable-models-of-larval-zebrafish-behavior | |
| Computationally and statistically efficient learning of causal Bayes nets using path queries | Kevin Bello | https://papers.nips.cc/paper/8290-computationally-and-statistically-efficient-learning-of-causal-bayes-nets-using-path-queries | |
| Sparse PCA from Sparse Linear Regression | Guy Bresler | https://papers.nips.cc/paper/8291-sparse-pca-from-sparse-linear-regression | |
| Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices | Don Dennis | https://papers.nips.cc/paper/8292-multiple-instance-learning-for-efficient-sequential-data-classification-on-resource-constrained-devices | |
| Transfer of Deep Reactive Policies for MDP Planning | Aniket (Nick) Bajpai | https://papers.nips.cc/paper/8293-transfer-of-deep-reactive-policies-for-mdp-planning | |
| The Price of Fair PCA: One Extra dimension | Samira Samadi | https://papers.nips.cc/paper/8294-the-price-of-fair-pca-one-extra-dimension | |
| GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking | Patrick Chen | https://papers.nips.cc/paper/8295-groupreduce-block-wise-low-rank-approximation-for-neural-language-model-shrinking |
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