| 2019 | A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. Jingkai Mao, Jakob N. Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson |
| 2019 | A Better k-means++ Algorithm via Local Search. Silvio Lattanzi, Christian Sohler |
| 2019 | A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation. Ramin Raziperchikolaei, Harish S. Bhat |
| 2019 | A Composite Randomized Incremental Gradient Method. Junyu Zhang, Lin Xiao |
| 2019 | A Conditional-Gradient-Based Augmented Lagrangian Framework. Alp Yurtsever, Olivier Fercoq, Volkan Cevher |
| 2019 | A Contrastive Divergence for Combining Variational Inference and MCMC. Francisco J. R. Ruiz, Michalis K. Titsias |
| 2019 | A Convergence Theory for Deep Learning via Over-Parameterization. Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song |
| 2019 | A Deep Reinforcement Learning Perspective on Internet Congestion Control. Nathan Jay, Noga H. Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar |
| 2019 | A Dynamical Systems Perspective on Nesterov Acceleration. Michael Muehlebach, Michael I. Jordan |
| 2019 | A Framework for Bayesian Optimization in Embedded Subspaces. Amin Nayebi, Alexander Munteanu, Matthias Poloczek |
| 2019 | A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng |
| 2019 | A Kernel Perspective for Regularizing Deep Neural Networks. Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal |
| 2019 | A Kernel Theory of Modern Data Augmentation. Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré |
| 2019 | A Large-Scale Study on Regularization and Normalization in GANs. Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly |
| 2019 | A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology. Onur Dereli, Ceyda Oguz, Mehmet Gönen |
| 2019 | A Persistent Weisfeiler-Lehman Procedure for Graph Classification. Bastian Rieck, Christian Bock, Karsten M. Borgwardt |
| 2019 | A Personalized Affective Memory Model for Improving Emotion Recognition. Pablo V. A. Barros, German Ignacio Parisi, Stefan Wermter |
| 2019 | A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes. Alireza Rezaei, Shayan Oveis Gharan |
| 2019 | A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. Yongqiang Cai, Qianxiao Li, Zuowei Shen |
| 2019 | A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion. Sylvain Lamprier |
| 2019 | A Statistical Investigation of Long Memory in Language and Music. Alexander Greaves-Tunnell, Zaïd Harchaoui |
| 2019 | A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban |
| 2019 | A Theoretical Analysis of Contrastive Unsupervised Representation Learning. Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar |
| 2019 | A Theory of Regularized Markov Decision Processes. Matthieu Geist, Bruno Scherrer, Olivier Pietquin |
| 2019 | A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii |
| 2019 | A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning. Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama |
| 2019 | A fully differentiable beam search decoder. Ronan Collobert, Awni Y. Hannun, Gabriel Synnaeve |
| 2019 | ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables. Mingzhang Yin, Yuguang Yue, Mingyuan Zhou |
| 2019 | AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer |
| 2019 | AUCμ: A Performance Metric for Multi-Class Machine Learning Models. Ross Kleiman, David Page |
| 2019 | Accelerated Flow for Probability Distributions. Amirhossein Taghvaei, Prashant G. Mehta |
| 2019 | Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu |
| 2019 | Acceleration of SVRG and Katyusha X by Inexact Preconditioning. Yanli Liu, Fei Feng, Wotao Yin |
| 2019 | Action Robust Reinforcement Learning and Applications in Continuous Control. Chen Tessler, Yonathan Efroni, Shie Mannor |
| 2019 | Active Embedding Search via Noisy Paired Comparisons. Gregory Canal, Andrew K. Massimino, Mark A. Davenport, Christopher J. Rozell |
| 2019 | Active Learning for Decision-Making from Imbalanced Observational Data. Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski |
| 2019 | Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. Sima Behpour, Anqi Liu, Brian D. Ziebart |
| 2019 | Active Learning with Disagreement Graphs. Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile |
| 2019 | Active Manifolds: A non-linear analogue to Active Subspaces. Robert A. Bridges, Anthony D. Gruber, Christopher Felder, Miki E. Verma, Chelsey Hoff |
| 2019 | Actor-Attention-Critic for Multi-Agent Reinforcement Learning. Shariq Iqbal, Fei Sha |
| 2019 | AdaGrad stepsizes: sharp convergence over nonconvex landscapes. Rachel A. Ward, Xiaoxia Wu, Léon Bottou |
| 2019 | Adaptive Antithetic Sampling for Variance Reduction. Hongyu Ren, Shengjia Zhao, Stefano Ermon |
| 2019 | Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits. Martin J. Zhang, James Zou, David Tse |
| 2019 | Adaptive Neural Trees. Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori |
| 2019 | Adaptive Regret of Convex and Smooth Functions. Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou |
| 2019 | Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth |
| 2019 | Adaptive Sensor Placement for Continuous Spaces. James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote |
| 2019 | Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida |
| 2019 | Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause |
| 2019 | Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment. Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Ángel Bautista, Shih-Yu Sun, Carlos Guestrin, Joshua M. Susskind |
| 2019 | Adjustment Criteria for Generalizing Experimental Findings. Juan D. Correa, Jin Tian, Elias Bareinboim |
| 2019 | Adversarial Attacks on Node Embeddings via Graph Poisoning. Aleksandar Bojchevski, Stephan Günnemann |
| 2019 | Adversarial Examples Are a Natural Consequence of Test Error in Noise. Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk |
| 2019 | Adversarial Generation of Time-Frequency Features with application in audio synthesis. Andrés Marafioti, Nathanaël Perraudin, Nicki Holighaus, Piotr Majdak |
| 2019 | Adversarial Online Learning with noise. Alon Resler, Yishay Mansour |
| 2019 | Adversarial camera stickers: A physical camera-based attack on deep learning systems. Juncheng Li, Frank R. Schmidt, J. Zico Kolter |
| 2019 | Adversarial examples from computational constraints. Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn |
| 2019 | Adversarially Learned Representations for Information Obfuscation and Inference. Martín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, Guillermo Sapiro |
| 2019 | Agnostic Federated Learning. Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh |
| 2019 | Almost Unsupervised Text to Speech and Automatic Speech Recognition. Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu |
| 2019 | Almost surely constrained convex optimization. Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher |
| 2019 | Alternating Minimizations Converge to Second-Order Optimal Solutions. Qiuwei Li, Zhihui Zhu, Gongguo Tang |
| 2019 | Amortized Monte Carlo Integration. Adam Golinski, Frank Wood, Tom Rainforth |
| 2019 | An Instability in Variational Inference for Topic Models. Behrooz Ghorbani, Hamid Javadi, Andrea Montanari |
| 2019 | An Investigation into Neural Net Optimization via Hessian Eigenvalue Density. Behrooz Ghorbani, Shankar Krishnan, Ying Xiao |
| 2019 | An Investigation of Model-Free Planning. Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap |
| 2019 | An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule. Touqir Sajed, Or Sheffet |
| 2019 | Analogies Explained: Towards Understanding Word Embeddings. Carl Allen, Timothy M. Hospedales |
| 2019 | Analyzing Federated Learning through an Adversarial Lens. Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo |
| 2019 | Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton |
| 2019 | Anomaly Detection With Multiple-Hypotheses Predictions. Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox |
| 2019 | Anytime Online-to-Batch, Optimism and Acceleration. Ashok Cutkosky |
| 2019 | Approximated Oracle Filter Pruning for Destructive CNN Width Optimization. Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan |
| 2019 | Approximating Orthogonal Matrices with Effective Givens Factorization. Thomas Frerix, Joan Bruna |
| 2019 | Approximation and non-parametric estimation of ResNet-type convolutional neural networks. Kenta Oono, Taiji Suzuki |
| 2019 | Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li, John Bradshaw, Yash Sharma |
| 2019 | Area Attention. Yang Li, Lukasz Kaiser, Samy Bengio, Si Si |
| 2019 | Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. Ahsan S. Alvi, Bin Xin Ru, Jan-Peter Calliess, Stephen J. Roberts, Michael A. Osborne |
| 2019 | AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss. Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson |
| 2019 | Automated Model Selection with Bayesian Quadrature. Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett |
| 2019 | Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth. Jacob Whitehill, Anand Ramakrishnan |
| 2019 | Automatic Posterior Transformation for Likelihood-Free Inference. David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke |
| 2019 | Autoregressive Energy Machines. Conor Durkan, Charlie Nash |
| 2019 | BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. Asa Cooper Stickland, Iain Murray |
| 2019 | Band-limited Training and Inference for Convolutional Neural Networks. Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin |
| 2019 | Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang |
| 2019 | Batch Policy Learning under Constraints. Hoang Minh Le, Cameron Voloshin, Yisong Yue |
| 2019 | BayesNAS: A Bayesian Approach for Neural Architecture Search. Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan |
| 2019 | Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling |
| 2019 | Bayesian Counterfactual Risk Minimization. Ben London, Ted Sandler |
| 2019 | Bayesian Deconditional Kernel Mean Embeddings. Kelvin Hsu, Fabio Ramos |
| 2019 | Bayesian Generative Active Deep Learning. Toan Tran, Thanh-Toan Do, Ian D. Reid, Gustavo Carneiro |
| 2019 | Bayesian Joint Spike-and-Slab Graphical Lasso. Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark |
| 2019 | Bayesian Nonparametric Federated Learning of Neural Networks. Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni |
| 2019 | Bayesian Optimization Meets Bayesian Optimal Stopping. Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2019 | Bayesian Optimization of Composite Functions. Raul Astudillo, Peter I. Frazier |
| 2019 | Bayesian leave-one-out cross-validation for large data. Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari |
| 2019 | Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. Julian Zimmert, Haipeng Luo, Chen-Yu Wei |
| 2019 | Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic |
| 2019 | Better generalization with less data using robust gradient descent. Matthew J. Holland, Kazushi Ikeda |
| 2019 | Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. Kaito Fujii, Shinsaku Sakaue |
| 2019 | Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf |
| 2019 | Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior. Fadhel Ayed, Juho Lee, Francois Caron |
| 2019 | Bias Also Matters: Bias Attribution for Deep Neural Network Explanation. Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes |
| 2019 | Bilinear Bandits with Low-rank Structure. Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak |
| 2019 | Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. Friso H. Kingma, Pieter Abbeel, Jonathan Ho |
| 2019 | Blended Conditonal Gradients. Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen J. Wright |
| 2019 | Boosted Density Estimation Remastered. Zac Cranko, Richard Nock |
| 2019 | Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii |
| 2019 | Breaking Inter-Layer Co-Adaptation by Classifier Anonymization. Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka |
| 2019 | Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. Octavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn |
| 2019 | Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms. Ashok Vardhan Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh |
| 2019 | Bridging Theory and Algorithm for Domain Adaptation. Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan |
| 2019 | CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims |
| 2019 | CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network. Tom Kenter, Vincent Wan, Chun-an Chan, Rob Clark, Jakub Vit |
| 2019 | COMIC: Multi-view Clustering Without Parameter Selection. Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou |
| 2019 | CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning. Cédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani |
| 2019 | Calibrated Approximate Bayesian Inference. Hanwen Xing, Geoff Nicholls, Jeong Lee |
| 2019 | Calibrated Model-Based Deep Reinforcement Learning. Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon |
| 2019 | CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. Gellért Weisz, András György, Csaba Szepesvári |
| 2019 | Categorical Feature Compression via Submodular Optimization. MohammadHossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh |
| 2019 | Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour |
| 2019 | Causal Identification under Markov Equivalence: Completeness Results. Amin Jaber, Jiji Zhang, Elias Bareinboim |
| 2019 | Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints. Nikolaos Liakopoulos, Apostolos Destounis, Georgios S. Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos |
| 2019 | Certified Adversarial Robustness via Randomized Smoothing. Jeremy Cohen, Elan Rosenfeld, J. Zico Kolter |
| 2019 | Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem |
| 2019 | Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan |
| 2019 | Characterizing Well-Behaved vs. Pathological Deep Neural Networks. Antoine Labatie |
| 2019 | Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group. Mario Lezcano Casado, David Martínez-Rubio |
| 2019 | Circuit-GNN: Graph Neural Networks for Distributed Circuit Design. Guo Zhang, Hao He, Dina Katabi |
| 2019 | Classification from Positive, Unlabeled and Biased Negative Data. Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama |
| 2019 | Classifying Treatment Responders Under Causal Effect Monotonicity. Nathan Kallus |
| 2019 | Co-Representation Network for Generalized Zero-Shot Learning. Fei Zhang, Guangming Shi |
| 2019 | Co-manifold learning with missing data. Gal Mishne, Eric C. Chi, Ronald R. Coifman |
| 2019 | CoT: Cooperative Training for Generative Modeling of Discrete Data. Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang |
| 2019 | Cognitive model priors for predicting human decisions. David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths |
| 2019 | Collaborative Channel Pruning for Deep Networks. Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang |
| 2019 | Collaborative Evolutionary Reinforcement Learning. Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer |
| 2019 | Collective Model Fusion for Multiple Black-Box Experts. Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford |
| 2019 | Combating Label Noise in Deep Learning using Abstention. Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof |
| 2019 | Combining parametric and nonparametric models for off-policy evaluation. Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez |
| 2019 | Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters. Jayadev Acharya, Ziteng Sun |
| 2019 | Communication-Constrained Inference and the Role of Shared Randomness. Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi |
| 2019 | CompILE: Compositional Imitation Learning and Execution. Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia |
| 2019 | Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu |
| 2019 | Complementary-Label Learning for Arbitrary Losses and Models. Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama |
| 2019 | Complexity of Linear Regions in Deep Networks. Boris Hanin, David Rolnick |
| 2019 | Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm. Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei |
| 2019 | Composing Entropic Policies using Divergence Correction. Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess |
| 2019 | Composing Value Functions in Reinforcement Learning. Benjamin van Niekerk, Steven James, Adam Christopher Earle, Benjamin Rosman |
| 2019 | Compositional Fairness Constraints for Graph Embeddings. Avishek Joey Bose, William L. Hamilton |
| 2019 | Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant |
| 2019 | Compressing Gradient Optimizers via Count-Sketches. Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava |
| 2019 | Concentration Inequalities for Conditional Value at Risk. Philip S. Thomas, Erik G. Learned-Miller |
| 2019 | Concrete Autoencoders: Differentiable Feature Selection and Reconstruction. Muhammed Fatih Balin, Abubakar Abid, James Y. Zou |
| 2019 | Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator. Alp Yurtsever, Suvrit Sra, Volkan Cevher |
| 2019 | Conditional Independence in Testing Bayesian Networks. Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche |
| 2019 | Conditioning by adaptive sampling for robust design. David H. Brookes, Hahnbeom Park, Jennifer Listgarten |
| 2019 | Connectivity-Optimized Representation Learning via Persistent Homology. Christoph D. Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit |
| 2019 | Context-Aware Zero-Shot Learning for Object Recognition. Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari |
| 2019 | Contextual Memory Trees. Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro |
| 2019 | Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model. Gi-Soo Kim, Myunghee Cho Paik |
| 2019 | Control Regularization for Reduced Variance Reinforcement Learning. Richard Cheng, Abhinav Verma, Gábor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick |
| 2019 | Convolutional Poisson Gamma Belief Network. Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou |
| 2019 | Coresets for Ordered Weighted Clustering. Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu |
| 2019 | Correlated Variational Auto-Encoders. Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi |
| 2019 | Correlated bandits or: How to minimize mean-squared error online. Vinay Praneeth Boda, Prashanth L. A. |
| 2019 | Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. Michael Oberst, David A. Sontag |
| 2019 | Counterfactual Visual Explanations. Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee |
| 2019 | Cross-Domain 3D Equivariant Image Embeddings. Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia |
| 2019 | Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty. Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim |
| 2019 | Curvature-Exploiting Acceleration of Elastic Net Computations. Vien V. Mai, Mikael Johansson |
| 2019 | DAG-GNN: DAG Structure Learning with Graph Neural Networks. Yue Yu, Jie Chen, Tian Gao, Mo Yu |
| 2019 | DBSCAN++: Towards fast and scalable density clustering. Jennifer Jang, Heinrich Jiang |
| 2019 | DL2: Training and Querying Neural Networks with Logic. Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin T. Vechev |
| 2019 | DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell |
| 2019 | Data Poisoning Attacks in Multi-Party Learning. Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed |
| 2019 | Data Poisoning Attacks on Stochastic Bandits. Fang Liu, Ness B. Shroff |
| 2019 | Data Shapley: Equitable Valuation of Data for Machine Learning. Amirata Ghorbani, James Y. Zou |
| 2019 | Dead-ends and Secure Exploration in Reinforcement Learning. Mehdi Fatemi, Shikhar Sharma, Harm van Seijen, Samira Ebrahimi Kahou |
| 2019 | Decentralized Exploration in Multi-Armed Bandits. Raphaël Féraud, Réda Alami, Romain Laroche |
| 2019 | Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |
| 2019 | Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models. Kaspar Märtens, Kieran R. Campbell, Christopher Yau |
| 2019 | Deep Compressed Sensing. Yan Wu, Mihaela Rosca, Timothy P. Lillicrap |
| 2019 | Deep Counterfactual Regret Minimization. Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm |
| 2019 | Deep Factors for Forecasting. Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski |
| 2019 | Deep Gaussian Processes with Importance-Weighted Variational Inference. Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth |
| 2019 | Deep Generative Learning via Variational Gradient Flow. Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang |
| 2019 | Deep Residual Output Layers for Neural Language Generation. Nikolaos Pappas, James Henderson |
| 2019 | DeepMDP: Learning Continuous Latent Space Models for Representation Learning. Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare |
| 2019 | DeepNose: Using artificial neural networks to represent the space of odorants. Ngoc B. Tran, Daniel R. Kepple, Sergey Shuvaev, Alexei A. Koulakov |
| 2019 | Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett |
| 2019 | Demystifying Dropout. Hongchang Gao, Jian Pei, Heng Huang |
| 2019 | Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm. Kejun Huang, Xiao Fu |
| 2019 | Diagnosing Bottlenecks in Deep Q-learning Algorithms. Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine |
| 2019 | Differentiable Dynamic Normalization for Learning Deep Representation. Ping Luo, Zhanglin Peng, Wenqi Shao, Ruimao Zhang, Jiamin Ren, Lingyun Wu |
| 2019 | Differentiable Linearized ADMM. Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin |
| 2019 | Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun |
| 2019 | Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. Di Wang, Changyou Chen, Jinhui Xu |
| 2019 | Differentially Private Fair Learning. Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman |
| 2019 | Differentially Private Learning of Geometric Concepts. Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer |
| 2019 | Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning. Seungyul Han, Youngchul Sung |
| 2019 | Dimensionality Reduction for Tukey Regression. Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff |
| 2019 | Direct Uncertainty Prediction for Medical Second Opinions. Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert D. Kleinberg, Sendhil Mullainathan, Jon M. Kleinberg |
| 2019 | Dirichlet Simplex Nest and Geometric Inference. Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen |
| 2019 | Discovering Conditionally Salient Features with Statistical Guarantees. Jaime Roquero Gimenez, James Y. Zou |
| 2019 | Discovering Context Effects from Raw Choice Data. Arjun Seshadri, Alex Peysakhovich, Johan Ugander |
| 2019 | Discovering Latent Covariance Structures for Multiple Time Series. Anh Tong, Jaesik Choi |
| 2019 | Discovering Options for Exploration by Minimizing Cover Time. Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris |
| 2019 | Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan |
| 2019 | Disentangled Graph Convolutional Networks. Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu |
| 2019 | Disentangling Disentanglement in Variational Autoencoders. Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh |
| 2019 | Distributed Learning over Unreliable Networks. Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu |
| 2019 | Distributed Learning with Sublinear Communication. Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan |
| 2019 | Distributed Weighted Matching via Randomized Composable Coresets. Sepehr Assadi, MohammadHossein Bateni, Vahab S. Mirrokni |
| 2019 | Distributed, Egocentric Representations of Graphs for Detecting Critical Structures. Ruo-Chun Tzeng, Shan-Hung Wu |
| 2019 | Distribution calibration for regression. Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach |
| 2019 | Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar |
| 2019 | Distributional Reinforcement Learning for Efficient Exploration. Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu |
| 2019 | Do ImageNet Classifiers Generalize to ImageNet? Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar |
| 2019 | Does Data Augmentation Lead to Positive Margin? Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos |
| 2019 | Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary C. Lipton |
| 2019 | Domain Agnostic Learning with Disentangled Representations. Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko |
| 2019 | DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression. Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu |
| 2019 | Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi |
| 2019 | Doubly-Competitive Distribution Estimation. Yi Hao, Alon Orlitsky |
| 2019 | Dropout as a Structured Shrinkage Prior. Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth |
| 2019 | Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication. Pedro Soto, Jun Li, Xiaodi Fan |
| 2019 | Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem. Junyu Cao, Wei Sun |
| 2019 | Dynamic Measurement Scheduling for Event Forecasting using Deep RL. Chun-Hao Chang, Mingjie Mai, Anna Goldenberg |
| 2019 | Dynamic Weights in Multi-Objective Deep Reinforcement Learning. Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher |
| 2019 | EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang |
| 2019 | ELF OpenGo: an analysis and open reimplementation of AlphaZero. Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick |
| 2019 | EMI: Exploration with Mutual Information. HyoungSeok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song |
| 2019 | Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds |
| 2019 | Efficient Dictionary Learning with Gradient Descent. Dar Gilboa, Sam Buchanan, John Wright |
| 2019 | Efficient Full-Matrix Adaptive Regularization. Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang |
| 2019 | Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. Quanming Yao, James Tin-Yau Kwok, Bo Han |
| 2019 | Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen |
| 2019 | Efficient On-Device Models using Neural Projections. Sujith Ravi |
| 2019 | Efficient Training of BERT by Progressively Stacking. Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu |
| 2019 | Efficient learning of smooth probability functions from Bernoulli tests with guarantees. Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher |
| 2019 | Efficient optimization of loops and limits with randomized telescoping sums. Alex Beatson, Ryan P. Adams |
| 2019 | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Mingxing Tan, Quoc V. Le |
| 2019 | EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang |
| 2019 | Emerging Convolutions for Generative Normalizing Flows. Emiel Hoogeboom, Rianne van den Berg, Max Welling |
| 2019 | Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models. Eldan Cohen, J. Christopher Beck |
| 2019 | End-to-End Probabilistic Inference for Nonstationary Audio Analysis. William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin |
| 2019 | Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi |
| 2019 | Equivariant Transformer Networks. Kai Sheng Tai, Peter Bailis, Gregory Valiant |
| 2019 | Error Feedback Fixes SignSGD and other Gradient Compression Schemes. Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi |
| 2019 | Escaping Saddle Points with Adaptive Gradient Methods. Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra |
| 2019 | Estimate Sequences for Variance-Reduced Stochastic Composite Optimization. Andrei Kulunchakov, Julien Mairal |
| 2019 | Estimating Information Flow in Deep Neural Networks. Ziv Goldfeld, Ewout van den Berg, Kristjan H. Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy |
| 2019 | Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation. Marco Ancona, Cengiz Öztireli, Markus H. Gross |
| 2019 | Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn |
| 2019 | Exploiting structure of uncertainty for efficient matroid semi-bandits. Pierre Perrault, Vianney Perchet, Michal Valko |
| 2019 | Exploration Conscious Reinforcement Learning Revisited. Lior Shani, Yonathan Efroni, Shie Mannor |
| 2019 | Exploring interpretable LSTM neural networks over multi-variable data. Tian Guo, Tao Lin, Nino Antulov-Fantulin |
| 2019 | Exploring the Landscape of Spatial Robustness. Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry |
| 2019 | Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum |
| 2019 | Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu |
| 2019 | Fair k-Center Clustering for Data Summarization. Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern |
| 2019 | Fairness risk measures. Robert C. Williamson, Aditya Krishna Menon |
| 2019 | Fairness without Harm: Decoupled Classifiers with Preference Guarantees. Berk Ustun, Yang Liu, David C. Parkes |
| 2019 | Fairness-Aware Learning for Continuous Attributes and Treatments. Jérémie Mary, Clément Calauzènes, Noureddine El Karoui |
| 2019 | Fairwashing: the risk of rationalization. Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp |
| 2019 | Fast Algorithm for Generalized Multinomial Models with Ranking Data. Jiaqi Gu, Guosheng Yin |
| 2019 | Fast Context Adaptation via Meta-Learning. Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson |
| 2019 | Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning. Weishi Shi, Qi Yu |
| 2019 | Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications. Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse |
| 2019 | Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise. Henry W. J. Reeve, Ata Kabán |
| 2019 | Fast and Flexible Inference of Joint Distributions from their Marginals. Charlie Frogner, Tomaso A. Poggio |
| 2019 | Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2019 | Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models. Chenyang Zhang, Guosheng Yin |
| 2019 | Faster Algorithms for Binary Matrix Factorization. Ravi Kumar, Rina Panigrahy, Ali Rahimi, David P. Woodruff |
| 2019 | Faster Attend-Infer-Repeat with Tractable Probabilistic Models. Karl Stelzner, Robert Peharz, Kristian Kersting |
| 2019 | Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization. Feihu Huang, Songcan Chen, Heng Huang |
| 2019 | Fault Tolerance in Iterative-Convergent Machine Learning. Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing |
| 2019 | Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data. Sergül Aydöre, Bertrand Thirion, Gaël Varoquaux |
| 2019 | Feature-Critic Networks for Heterogeneous Domain Generalization. Yiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales |
| 2019 | Finding Mixed Nash Equilibria of Generative Adversarial Networks. Ya-Ping Hsieh, Chen Liu, Volkan Cevher |
| 2019 | Finding Options that Minimize Planning Time. Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris |
| 2019 | Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang |
| 2019 | Fingerprint Policy Optimisation for Robust Reinforcement Learning. Supratik Paul, Michael A. Osborne, Shimon Whiteson |
| 2019 | Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. Thinh T. Doan, Siva Theja Maguluri, Justin Romberg |
| 2019 | First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz |
| 2019 | First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems. Ching-pei Lee, Stephen J. Wright |
| 2019 | Flat Metric Minimization with Applications in Generative Modeling. Thomas Möllenhoff, Daniel Cremers |
| 2019 | Flexibly Fair Representation Learning by Disentanglement. Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel |
| 2019 | FloWaveNet : A Generative Flow for Raw Audio. Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon |
| 2019 | Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design. Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel |
| 2019 | Formal Privacy for Functional Data with Gaussian Perturbations. Ardalan Mirshani, Matthew Reimherr, Aleksandra B. Slavkovic |
| 2019 | Functional Transparency for Structured Data: a Game-Theoretic Approach. Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola |
| 2019 | GDPP: Learning Diverse Generations using Determinantal Point Processes. Mohamed Elfeki, Camille Couprie, Morgane Rivière, Mohamed Elhoseiny |
| 2019 | GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects. Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger |
| 2019 | GMNN: Graph Markov Neural Networks. Meng Qu, Yoshua Bengio, Jian Tang |
| 2019 | GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. David John, Vincent Heuveline, Michael Schober |
| 2019 | Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute. Tong Wang |
| 2019 | Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function. Arvind U. Raghunathan, Anoop Cherian, Devesh K. Jha |
| 2019 | Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh |
| 2019 | Gauge Equivariant Convolutional Networks and the Icosahedral CNN. Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling |
| 2019 | Generalized Approximate Survey Propagation for High-Dimensional Estimation. Carlo Lucibello, Luca Saglietti, Yue M. Lu |
| 2019 | Generalized Linear Rule Models. Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük |
| 2019 | Generalized Majorization-Minimization. Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro F. Felzenszwalb |
| 2019 | Generalized No Free Lunch Theorem for Adversarial Robustness. Elvis Dohmatob |
| 2019 | Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song |
| 2019 | Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation. Jinyang Yuan, Bin Li, Xiangyang Xue |
| 2019 | Geometric Losses for Distributional Learning. Arthur Mensch, Mathieu Blondel, Gabriel Peyré |
| 2019 | Geometric Scattering for Graph Data Analysis. Feng Gao, Guy Wolf, Matthew J. Hirn |
| 2019 | Geometry Aware Convolutional Filters for Omnidirectional Images Representation. Renata Khasanova, Pascal Frossard |
| 2019 | Geometry and Symmetry in Short-and-Sparse Deconvolution. Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright |
| 2019 | Global Convergence of Block Coordinate Descent in Deep Learning. Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao |
| 2019 | Good Initializations of Variational Bayes for Deep Models. Simone Rossi, Pietro Michiardi, Maurizio Filippone |
| 2019 | Gradient Descent Finds Global Minima of Deep Neural Networks. Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai |
| 2019 | Graph Convolutional Gaussian Processes. Ian Walker, Ben Glocker |
| 2019 | Graph Element Networks: adaptive, structured computation and memory. Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauzá Villalonga, Alberto Rodriguez, Tomás Lozano-Pérez, Leslie Pack Kaelbling |
| 2019 | Graph Matching Networks for Learning the Similarity of Graph Structured Objects. Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli |
| 2019 | Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam |
| 2019 | Graph Resistance and Learning from Pairwise Comparisons. Julien M. Hendrickx, Alexander Olshevsky, Venkatesh Saligrama |
| 2019 | Graph U-Nets. Hongyang Gao, Shuiwang Ji |
| 2019 | Graphical-model based estimation and inference for differential privacy. Ryan McKenna, Daniel Sheldon, Gerome Miklau |
| 2019 | Graphite: Iterative Generative Modeling of Graphs. Aditya Grover, Aaron Zweig, Stefano Ermon |
| 2019 | Greedy Layerwise Learning Can Scale To ImageNet. Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon |
| 2019 | Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization. Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang |
| 2019 | Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang |
| 2019 | Gromov-Wasserstein Learning for Graph Matching and Node Embedding. Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin |
| 2019 | Guarantees for Spectral Clustering with Fairness Constraints. Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern |
| 2019 | Guided evolutionary strategies: augmenting random search with surrogate gradients. Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein |
| 2019 | HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving. Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox |
| 2019 | Hessian Aided Policy Gradient. Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi |
| 2019 | Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin. Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou |
| 2019 | HexaGAN: Generative Adversarial Nets for Real World Classification. Uiwon Hwang, Dahuin Jung, Sungroh Yoon |
| 2019 | Hierarchical Decompositional Mixtures of Variational Autoencoders. Ping Liang Tan, Robert Peharz |
| 2019 | Hierarchical Importance Weighted Autoencoders. Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron C. Courville |
| 2019 | Hierarchically Structured Meta-learning. Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li |
| 2019 | High-Fidelity Image Generation With Fewer Labels. Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly |
| 2019 | Hiring Under Uncertainty. Manish Purohit, Sreenivas Gollapudi, Manish Raghavan |
| 2019 | Homomorphic Sensing. Manolis C. Tsakiris, Liangzu Peng |
| 2019 | How does Disagreement Help Generalization against Label Corruption? Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama |
| 2019 | Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops. Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai |
| 2019 | Hybrid Models with Deep and Invertible Features. Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan |
| 2019 | HyperGAN: A Generative Model for Diverse, Performant Neural Networks. Neale Ratzlaff, Fuxin Li |
| 2019 | Hyperbolic Disk Embeddings for Directed Acyclic Graphs. Ryota Suzuki, Ryusuke Takahama, Shun Onoda |
| 2019 | IMEXnet A Forward Stable Deep Neural Network. Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto |
| 2019 | Imitating Latent Policies from Observation. Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell Jr. |
| 2019 | Imitation Learning from Imperfect Demonstration. Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama |
| 2019 | Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel |
| 2019 | Importance Sampling Policy Evaluation with an Estimated Behavior Policy. Josiah Hanna, Scott Niekum, Peter Stone |
| 2019 | Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares. Alina Ene, Adrian Vladu |
| 2019 | Improved Dynamic Graph Learning through Fault-Tolerant Sparsification. Chun Jiang Zhu, Sabine Storandt, Kam-yiu Lam, Song Han, Jinbo Bi |
| 2019 | Improved Parallel Algorithms for Density-Based Network Clustering. Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrovic |
| 2019 | Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang |
| 2019 | Improving Adversarial Robustness via Promoting Ensemble Diversity. Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu |
| 2019 | Improving Model Selection by Employing the Test Data. Max Westphal, Werner Brannath |
| 2019 | Improving Neural Language Modeling via Adversarial Training. Dilin Wang, Chengyue Gong, Qiang Liu |
| 2019 | Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang |
| 2019 | Imputing Missing Events in Continuous-Time Event Streams. Hongyuan Mei, Guanghui Qin, Jason Eisner |
| 2019 | Incorporating Grouping Information into Bayesian Decision Tree Ensembles. Junliang Du, Antonio R. Linero |
| 2019 | Incremental Randomized Sketching for Online Kernel Learning. Xiao Zhang, Shizhong Liao |
| 2019 | Inference and Sampling of $K_33$-free Ising Models. Valerii Likhosherstov, Yury Maximov, Misha Chertkov |
| 2019 | Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. Muhammad Osama, Dave Zachariah, Thomas B. Schön |
| 2019 | Infinite Mixture Prototypes for Few-shot Learning. Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum |
| 2019 | Information-Theoretic Considerations in Batch Reinforcement Learning. Jinglin Chen, Nan Jiang |
| 2019 | Insertion Transformer: Flexible Sequence Generation via Insertion Operations. Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit |
| 2019 | Interpreting Adversarially Trained Convolutional Neural Networks. Tianyuan Zhang, Zhanxing Zhu |
| 2019 | Invariant-Equivariant Representation Learning for Multi-Class Data. Ilya Feige |
| 2019 | Invertible Residual Networks. Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen |
| 2019 | Iterative Linearized Control: Stable Algorithms and Complexity Guarantees. Vincent Roulet, Dmitriy Drusvyatskiy, Siddhartha S. Srinivasa, Zaïd Harchaoui |
| 2019 | Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin |
| 2019 | Jumpout : Improved Dropout for Deep Neural Networks with ReLUs. Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes |
| 2019 | Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang |
| 2019 | Kernel Mean Matching for Content Addressability of GANs. Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf |
| 2019 | Kernel Normalized Cut: a Theoretical Revisit. Yoshikazu Terada, Michio Yamamoto |
| 2019 | Kernel-Based Reinforcement Learning in Robust Markov Decision Processes. Shiau Hong Lim, Arnaud Autef |
| 2019 | LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu |
| 2019 | LIT: Learned Intermediate Representation Training for Model Compression. Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia |
| 2019 | LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick |
| 2019 | Ladder Capsule Network. Taewon Jeong, Youngmin Lee, Heeyoung Kim |
| 2019 | Large-Scale Sparse Kernel Canonical Correlation Analysis. Viivi Uurtio, Sahely Bhadra, Juho Rousu |
| 2019 | Latent Normalizing Flows for Discrete Sequences. Zachary M. Ziegler, Alexander M. Rush |
| 2019 | LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. Songyang Zhang, Xuming He, Shipeng Yan |
| 2019 | Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting. Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong |
| 2019 | Learning Action Representations for Reinforcement Learning. Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas |
| 2019 | Learning Classifiers for Target Domain with Limited or No Labels. Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama |
| 2019 | Learning Context-dependent Label Permutations for Multi-label Classification. Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz |
| 2019 | Learning Dependency Structures for Weak Supervision Models. Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré |
| 2019 | Learning Discrete Structures for Graph Neural Networks. Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He |
| 2019 | Learning Discrete and Continuous Factors of Data via Alternating Disentanglement. Yeonwoo Jeong, Hyun Oh Song |
| 2019 | Learning Distance for Sequences by Learning a Ground Metric. Bing Su, Ying Wu |
| 2019 | Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré |
| 2019 | Learning Generative Models across Incomparable Spaces. Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka |
| 2019 | Learning Hawkes Processes Under Synchronization Noise. William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran |
| 2019 | Learning Latent Dynamics for Planning from Pixels. Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson |
| 2019 | Learning Linear-Quadratic Regulators Efficiently with only √T Regret. Alon Cohen, Tomer Koren, Yishay Mansour |
| 2019 | Learning Models from Data with Measurement Error: Tackling Underreporting. Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria |
| 2019 | Learning Neurosymbolic Generative Models via Program Synthesis. Halley Young, Osbert Bastani, Mayur Naik |
| 2019 | Learning Novel Policies For Tasks. Yunbo Zhang, Wenhao Yu, Greg Turk |
| 2019 | Learning Optimal Fair Policies. Razieh Nabi, Daniel Malinsky, Ilya Shpitser |
| 2019 | Learning Optimal Linear Regularizers. Matthew Streeter |
| 2019 | Learning Structured Decision Problems with Unawareness. Craig Innes, Alex Lascarides |
| 2019 | Learning What and Where to Transfer. Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin |
| 2019 | Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar |
| 2019 | Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. Kelvin Xu, Ellis Ratner, Anca D. Dragan, Sergey Levine, Chelsea Finn |
| 2019 | Learning and Data Selection in Big Datasets. Hossein Shokri Ghadikolaei, Hadi G. Ghauch, Carlo Fischione, Mikael Skoglund |
| 2019 | Learning deep kernels for exponential family densities. Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton |
| 2019 | Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan |
| 2019 | Learning from a Learner. Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin |
| 2019 | Learning interpretable continuous-time models of latent stochastic dynamical systems. Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani |
| 2019 | Learning to Clear the Market. Weiran Shen, Sébastien Lahaie, Renato Paes Leme |
| 2019 | Learning to Collaborate in Markov Decision Processes. Goran Radanovic, Rati Devidze, David C. Parkes, Adish Singla |
| 2019 | Learning to Convolve: A Generalized Weight-Tying Approach. Nichita Diaconu, Daniel E. Worrall |
| 2019 | Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. Lingbing Guo, Zequn Sun, Wei Hu |
| 2019 | Learning to Generalize from Sparse and Underspecified Rewards. Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi |
| 2019 | Learning to Groove with Inverse Sequence Transformations. Jon Gillick, Adam Roberts, Jesse H. Engel, Douglas Eck, David Bamman |
| 2019 | Learning to Infer Program Sketches. Maxwell I. Nye, Luke B. Hewitt, Joshua B. Tenenbaum, Armando Solar-Lezama |
| 2019 | Learning to Optimize Multigrid PDE Solvers. Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel |
| 2019 | Learning to Prove Theorems via Interacting with Proof Assistants. Kaiyu Yang, Jia Deng |
| 2019 | Learning to Route in Similarity Graphs. Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko |
| 2019 | Learning to bid in revenue-maximizing auctions. Thomas Nedelec, Noureddine El Karoui, Vianney Perchet |
| 2019 | Learning to select for a predefined ranking. Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov |
| 2019 | Learning with Bad Training Data via Iterative Trimmed Loss Minimization. Yanyao Shen, Sujay Sanghavi |
| 2019 | Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil |
| 2019 | LegoNet: Efficient Convolutional Neural Networks with Lego Filters. Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu |
| 2019 | Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto |
| 2019 | Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry |
| 2019 | Linear-Complexity Data-Parallel Earth Mover's Distance Approximations. Kubilay Atasu, Thomas Mittelholzer |
| 2019 | Lipschitz Generative Adversarial Nets. Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang |
| 2019 | Locally Private Bayesian Inference for Count Models. Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach |
| 2019 | Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. Tahrima Rahman, Shasha Jin, Vibhav Gogate |
| 2019 | Lorentzian Distance Learning for Hyperbolic Representations. Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel |
| 2019 | Loss Landscapes of Regularized Linear Autoencoders. Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed |
| 2019 | Lossless or Quantized Boosting with Integer Arithmetic. Richard Nock, Robert C. Williamson |
| 2019 | Low Latency Privacy Preserving Inference. Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha |
| 2019 | Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. Dongruo Zhou, Quanquan Gu |
| 2019 | MASS: Masked Sequence to Sequence Pre-training for Language Generation. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu |
| 2019 | ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation. Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi |
| 2019 | MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. Pierre-Alexandre Mattei, Jes Frellsen |
| 2019 | MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means. Matthieu Lerasle, Zoltán Szabó, Timothée Mathieu, Guillaume Lecué |
| 2019 | Making Convolutional Networks Shift-Invariant Again. Richard Zhang |
| 2019 | Making Decisions that Reduce Discriminatory Impacts. Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva |
| 2019 | Making Deep Q-learning methods robust to time discretization. Corentin Tallec, Léonard Blier, Yann Ollivier |
| 2019 | Mallows ranking models: maximum likelihood estimate and regeneration. Wenpin Tang |
| 2019 | Manifold Mixup: Better Representations by Interpolating Hidden States. Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio |
| 2019 | Matrix-Free Preconditioning in Online Learning. Ashok Cutkosky, Tamás Sarlós |
| 2019 | Maximum Entropy-Regularized Multi-Goal Reinforcement Learning. Rui Zhao, Xudong Sun, Volker Tresp |
| 2019 | Maximum Likelihood Estimation for Learning Populations of Parameters. Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade |
| 2019 | MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. Eric Chu, Peter J. Liu |
| 2019 | Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians. Vardan Papyan |
| 2019 | Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. Albert Gural, Boris Murmann |
| 2019 | Meta-Learning Neural Bloom Filters. Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap |
| 2019 | Metric-Optimized Example Weights. Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya R. Gupta |
| 2019 | MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement. Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin |
| 2019 | Metropolis-Hastings Generative Adversarial Networks. Ryan D. Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski |
| 2019 | Minimal Achievable Sufficient Statistic Learning. Milan Cvitkovic, Günther Koliander |
| 2019 | MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan |
| 2019 | Mixture Models for Diverse Machine Translation: Tricks of the Trade. Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato |
| 2019 | Model Comparison for Semantic Grouping. Francisco Vargas, Kamen Brestnichki, Nils Hammerla |
| 2019 | Model Function Based Conditional Gradient Method with Armijo-like Line Search. Peter Ochs, Yura Malitsky |
| 2019 | Model-Based Active Exploration. Pranav Shyam, Wojciech Jaskowski, Faustino Gomez |
| 2019 | Molecular Hypergraph Grammar with Its Application to Molecular Optimization. Hiroshi Kajino |
| 2019 | Moment-Based Variational Inference for Markov Jump Processes. Christian Wildner, Heinz Koeppl |
| 2019 | Monge blunts Bayes: Hardness Results for Adversarial Training. Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder |
| 2019 | More Efficient Off-Policy Evaluation through Regularized Targeted Learning. Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan |
| 2019 | Multi-Agent Adversarial Inverse Reinforcement Learning. Lantao Yu, Jiaming Song, Stefano Ermon |
| 2019 | Multi-Frequency Phase Synchronization. Tingran Gao, Zhizhen Zhao |
| 2019 | Multi-Frequency Vector Diffusion Maps. Yifeng Fan, Zhizhen Zhao |
| 2019 | Multi-Object Representation Learning with Iterative Variational Inference. Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner |
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| 2019 | Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always. Ioannis Panageas, Georgios Piliouras, Xiao Wang |
| 2019 | Multivariate Submodular Optimization. Richard Santiago, F. Bruce Shepherd |
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| 2019 | NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong |
| 2019 | Natural Analysts in Adaptive Data Analysis. Tijana Zrnic, Moritz Hardt |
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| 2019 | Neural Inverse Knitting: From Images to Manufacturing Instructions. Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik |
| 2019 | Neural Joint Source-Channel Coding. Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon |
| 2019 | Neural Logic Reinforcement Learning. Zhengyao Jiang, Shan Luo |
| 2019 | Neural Network Attributions: A Causal Perspective. Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian |
| 2019 | Neural Separation of Observed and Unobserved Distributions. Tavi Halperin, Ariel Ephrat, Yedid Hoshen |
| 2019 | Neurally-Guided Structure Inference. Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu |
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| 2019 | Noise2Self: Blind Denoising by Self-Supervision. Joshua Batson, Loïc Royer |
| 2019 | Noisy Dual Principal Component Pursuit. Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal |
| 2019 | Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization. Thanh Huy Nguyen, Umut Simsekli, Gaël Richard |
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| 2019 | Non-Parametric Priors For Generative Adversarial Networks. Rajhans Singh, Pavan K. Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun |
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| 2019 | Nonconvex Variance Reduced Optimization with Arbitrary Sampling. Samuel Horváth, Peter Richtárik |
| 2019 | Nonlinear Distributional Gradient Temporal-Difference Learning. Chao Qu, Shie Mannor, Huan Xu |
| 2019 | Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. Dilin Wang, Qiang Liu |
| 2019 | Nonparametric Bayesian Deep Networks with Local Competition. Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis |
| 2019 | Obtaining Fairness using Optimal Transport Theory. Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes |
| 2019 | Off-Policy Deep Reinforcement Learning without Exploration. Scott Fujimoto, David Meger, Doina Precup |
| 2019 | On Certifying Non-Uniform Bounds against Adversarial Attacks. Chen Liu, Ryota Tomioka, Volkan Cevher |
| 2019 | On Connected Sublevel Sets in Deep Learning. Quynh Nguyen |
| 2019 | On Dropout and Nuclear Norm Regularization. Poorya Mianjy, Raman Arora |
| 2019 | On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. Tianyi Lin, Nhat Ho, Michael I. Jordan |
| 2019 | On Learning Invariant Representations for Domain Adaptation. Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon |
| 2019 | On Medians of (Randomized) Pairwise Means. Stéphan Clémençon, Pierre Laforgue, Patrice Bertail |
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| 2019 | On Sparse Linear Regression in the Local Differential Privacy Model. Di Wang, Jinhui Xu |
| 2019 | On Symmetric Losses for Learning from Corrupted Labels. Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama |
| 2019 | On The Power of Curriculum Learning in Training Deep Networks. Guy Hacohen, Daphna Weinshall |
| 2019 | On Variational Bounds of Mutual Information. Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker |
| 2019 | On discriminative learning of prediction uncertainty. Vojtech Franc, Daniel Prusa |
| 2019 | On the Complexity of Approximating Wasserstein Barycenters. Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Cesar A. Uribe |
| 2019 | On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization. Hao Yu, Rong Jin |
| 2019 | On the Connection Between Adversarial Robustness and Saliency Map Interpretability. Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb |
| 2019 | On the Convergence and Robustness of Adversarial Training. Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu |
| 2019 | On the Design of Estimators for Bandit Off-Policy Evaluation. Nikos Vlassis, Aurélien Bibaut, Maria Dimakopoulou, Tony Jebara |
| 2019 | On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference. Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca D. Dragan |
| 2019 | On the Generalization Gap in Reparameterizable Reinforcement Learning. Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher |
| 2019 | On the Impact of the Activation function on Deep Neural Networks Training. Soufiane Hayou, Arnaud Doucet, Judith Rousseau |
| 2019 | On the Limitations of Representing Functions on Sets. Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne |
| 2019 | On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. Hao Yu, Rong Jin, Sen Yang |
| 2019 | On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning. Hoda Heidari, Vedant Nanda, Krishna P. Gummadi |
| 2019 | On the Spectral Bias of Neural Networks. Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville |
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| 2019 | Online Adaptive Principal Component Analysis and Its extensions. Jianjun Yuan, Andrew G. Lamperski |
| 2019 | Online Algorithms for Rent-Or-Buy with Expert Advice. Sreenivas Gollapudi, Debmalya Panigrahi |
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| 2019 | Online Convex Optimization in Adversarial Markov Decision Processes. Aviv Rosenberg, Yishay Mansour |
| 2019 | Online Learning to Rank with Features. Shuai Li, Tor Lattimore, Csaba Szepesvári |
| 2019 | Online Learning with Sleeping Experts and Feedback Graphs. Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang |
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| 2019 | Optimal Mini-Batch and Step Sizes for SAGA. Nidham Gazagnadou, Robert M. Gower, Joseph Salmon |
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| 2019 | PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization. Songtao Lu, Mingyi Hong, Zhengdao Wang |
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| 2019 | Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA Kamalika Chaudhuri, Ruslan Salakhutdinov |
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| 2019 | Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions. Hao Wang, Berk Ustun, Flávio P. Calmon |
| 2019 | Replica Conditional Sequential Monte Carlo. Alexander Y. Shestopaloff, Arnaud Doucet |
| 2019 | Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. Yochai Blau, Tomer Michaeli |
| 2019 | Revisiting precision recall definition for generative modeling. Loïc Simon, Ryan Webster, Julien Rabin |
| 2019 | Revisiting the Softmax Bellman Operator: New Benefits and New Perspective. Zhao Song, Ronald Parr, Lawrence Carin |
| 2019 | Riemannian adaptive stochastic gradient algorithms on matrix manifolds. Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra |
| 2019 | Robust Decision Trees Against Adversarial Examples. Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh |
| 2019 | Robust Estimation of Tree Structured Gaussian Graphical Models. Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis |
| 2019 | Robust Inference via Generative Classifiers for Handling Noisy Labels. Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin |
| 2019 | Robust Influence Maximization for Hyperparametric Models. Dimitris Kalimeris, Gal Kaplun, Yaron Singer |
| 2019 | Robust Learning from Untrusted Sources. Nikola Konstantinov, Christoph Lampert |
| 2019 | Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. Raphael Suter, Ðorðe Miladinovic, Bernhard Schölkopf, Stefan Bauer |
| 2019 | Rotation Invariant Householder Parameterization for Bayesian PCA. Rajbir-Singh Nirwan, Nils Bertschinger |
| 2019 | SAGA with Arbitrary Sampling. Xun Qian, Zheng Qu, Peter Richtárik |
| 2019 | SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. Po-Wei Wang, Priya L. Donti, Bryan Wilder, J. Zico Kolter |
| 2019 | SELFIE: Refurbishing Unclean Samples for Robust Deep Learning. Hwanjun Song, Minseok Kim, Jae-Gil Lee |
| 2019 | SGD with Arbitrary Sampling: General Analysis and Improved Rates. Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin |
| 2019 | SGD without Replacement: Sharper Rates for General Smooth Convex Functions. Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli |
| 2019 | SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine |
| 2019 | SWALP : Stochastic Weight Averaging in Low Precision Training. Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa |
| 2019 | Safe Grid Search with Optimal Complexity. Eugène Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi |
| 2019 | Safe Policy Improvement with Baseline Bootstrapping. Romain Laroche, Paul Trichelair, Remi Tachet des Combes |
| 2019 | Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization. Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman |
| 2019 | Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. Lin Yang, Mengdi Wang |
| 2019 | Scalable Fair Clustering. Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner |
| 2019 | Scalable Learning in Reproducing Kernel Krein Spaces. Dino Oglic, Thomas Gärtner |
| 2019 | Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet |
| 2019 | Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. Edwin Fong, Simon Lyddon, Chris C. Holmes |
| 2019 | Scalable Training of Inference Networks for Gaussian-Process Models. Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu |
| 2019 | Scale-free adaptive planning for deterministic dynamics & discounted rewards. Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko |
| 2019 | Scaling Up Ordinal Embedding: A Landmark Approach. Jesse Anderton, Javed A. Aslam |
| 2019 | Screening rules for Lasso with non-convex Sparse Regularizers. Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon |
| 2019 | SelectiveNet: A Deep Neural Network with an Integrated Reject Option. Yonatan Geifman, Ran El-Yaniv |
| 2019 | Self-Attention Generative Adversarial Networks. Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena |
| 2019 | Self-Attention Graph Pooling. Junhyun Lee, Inyeop Lee, Jaewoo Kang |
| 2019 | Self-Supervised Exploration via Disagreement. Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta |
| 2019 | Self-similar Epochs: Value in arrangement. Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias |
| 2019 | Semi-Cyclic Stochastic Gradient Descent. Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar |
| 2019 | Sensitivity Analysis of Linear Structural Causal Models. Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim |
| 2019 | Separable value functions across time-scales. Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill |
| 2019 | Sequential Facility Location: Approximate Submodularity and Greedy Algorithm. Ehsan Elhamifar |
| 2019 | Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh |
| 2019 | Sever: A Robust Meta-Algorithm for Stochastic Optimization. Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart |
| 2019 | Shallow-Deep Networks: Understanding and Mitigating Network Overthinking. Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras |
| 2019 | Shape Constraints for Set Functions. Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu |
| 2019 | Similarity of Neural Network Representations Revisited. Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton |
| 2019 | Simple Black-box Adversarial Attacks. Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger |
| 2019 | Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. Michael R. Metel, Akiko Takeda |
| 2019 | Simplifying Graph Convolutional Networks. Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Q. Weinberger |
| 2019 | Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter |
| 2019 | Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas |
| 2019 | Sorting Out Lipschitz Function Approximation. Cem Anil, James Lucas, Roger B. Grosse |
| 2019 | Sparse Extreme Multi-label Learning with Oracle Property. Weiwei Liu, Xiaobo Shen |
| 2019 | Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi |
| 2019 | Spectral Approximate Inference. Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin |
| 2019 | Spectral Clustering of Signed Graphs via Matrix Power Means. Pedro Mercado, Francesco Tudisco, Matthias Hein |
| 2019 | Stable and Fair Classification. Lingxiao Huang, Nisheeth K. Vishnoi |
| 2019 | Stable-Predictive Optimistic Counterfactual Regret Minimization. Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm |
| 2019 | State-Regularized Recurrent Neural Networks. Cheng Wang, Mathias Niepert |
| 2019 | State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer |
| 2019 | Static Automatic Batching In TensorFlow. Ashish Agarwal |
| 2019 | Statistical Foundations of Virtual Democracy. Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel D. Procaccia, Christos-Alexandros Psomas |
| 2019 | Statistics and Samples in Distributional Reinforcement Learning. Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney |
| 2019 | Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging. Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar |
| 2019 | Stein Point Markov Chain Monte Carlo. Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates |
| 2019 | Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. Wouter Kool, Herke van Hoof, Max Welling |
| 2019 | Stochastic Blockmodels meet Graph Neural Networks. Nikhil Mehta, Lawrence Carin, Piyush Rai |
| 2019 | Stochastic Deep Networks. Gwendoline de Bie, Gabriel Peyré, Marco Cuturi |
| 2019 | Stochastic Gradient Push for Distributed Deep Learning. Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat |
| 2019 | Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. Baojian Zhou, Feng Chen, Yiming Ying |
| 2019 | Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang |
| 2019 | Structured agents for physical construction. Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick |
| 2019 | Sublinear Space Private Algorithms Under the Sliding Window Model. Jalaj Upadhyay |
| 2019 | Sublinear Time Nearest Neighbor Search over Generalized Weighted Space. Yifan Lei, Qiang Huang, Mohan S. Kankanhalli, Anthony K. H. Tung |
| 2019 | Sublinear quantum algorithms for training linear and kernel-based classifiers. Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu |
| 2019 | Submodular Cost Submodular Cover with an Approximate Oracle. Victoria G. Crawford, Alan Kuhnle, My T. Thai |
| 2019 | Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications. Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi |
| 2019 | Submodular Observation Selection and Information Gathering for Quadratic Models. Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu |
| 2019 | Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity. Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi |
| 2019 | Subspace Robust Wasserstein Distances. François-Pierre Paty, Marco Cuturi |
| 2019 | Sum-of-Squares Polynomial Flow. Priyank Jaini, Kira A. Selby, Yaoliang Yu |
| 2019 | Supervised Hierarchical Clustering with Exponential Linkage. Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum |
| 2019 | Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona |
| 2019 | Switching Linear Dynamics for Variational Bayes Filtering. Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt |
| 2019 | Taming MAML: Efficient unbiased meta-reinforcement learning. Hao Liu, Richard Socher, Caiming Xiong |
| 2019 | TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning. Sung Whan Yoon, Jun Seo, Jaekyun Moon |
| 2019 | TarMAC: Targeted Multi-Agent Communication. Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau |
| 2019 | Target Tracking for Contextual Bandits: Application to Demand Side Management. Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz |
| 2019 | Target-Based Temporal-Difference Learning. Donghwan Lee, Niao He |
| 2019 | Task-Agnostic Dynamics Priors for Deep Reinforcement Learning. Yilun Du, Karthik Narasimhan |
| 2019 | Teaching a black-box learner. Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu |
| 2019 | Temporal Gaussian Mixture Layer for Videos. A. J. Piergiovanni, Michael S. Ryoo |
| 2019 | Tensor Variable Elimination for Plated Factor Graphs. Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin T. Chiu, Alexander M. Rush, Noah D. Goodman |
| 2019 | TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. Augustus Odena, Catherine Olsson, David G. Andersen, Ian J. Goodfellow |
| 2019 | The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma |
| 2019 | The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith |
| 2019 | The Evolved Transformer. David R. So, Quoc V. Le, Chen Liang |
| 2019 | The Implicit Fairness Criterion of Unconstrained Learning. Lydia T. Liu, Max Simchowitz, Moritz Hardt |
| 2019 | The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick |
| 2019 | The Natural Language of Actions. Guy Tennenholtz, Shie Mannor |
| 2019 | The Odds are Odd: A Statistical Test for Detecting Adversarial Examples. Kevin Roth, Yannic Kilcher, Thomas Hofmann |
| 2019 | The Value Function Polytope in Reinforcement Learning. Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans |
| 2019 | The Variational Predictive Natural Gradient. Da Tang, Rajesh Ranganath |
| 2019 | The Wasserstein Transform. Facundo Mémoli, Zane T. Smith, Zhengchao Wan |
| 2019 | The advantages of multiple classes for reducing overfitting from test set reuse. Vitaly Feldman, Roy Frostig, Moritz Hardt |
| 2019 | The information-theoretic value of unlabeled data in semi-supervised learning. Alexander Golovnev, Dávid Pál, Balázs Szörényi |
| 2019 | Theoretically Principled Trade-off between Robustness and Accuracy. Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan |
| 2019 | TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning. Tameem Adel, Adrian Weller |
| 2019 | Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering. Taisuke Yasuda, David P. Woodruff, Manuel Fernandez |
| 2019 | Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. Andrea Zanette, Emma Brunskill |
| 2019 | Topological Data Analysis of Decision Boundaries with Application to Model Selection. Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody |
| 2019 | Toward Controlling Discrimination in Online Ad Auctions. L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi |
| 2019 | Toward Understanding the Importance of Noise in Training Neural Networks. Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao |
| 2019 | Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan |
| 2019 | Towards Understanding Knowledge Distillation. Mary Phuong, Christoph Lampert |
| 2019 | Towards a Deep and Unified Understanding of Deep Neural Models in NLP. Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie |
| 2019 | Towards a Unified Analysis of Random Fourier Features. Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic |
| 2019 | Tractable n-Metrics for Multiple Graphs. Sam Safavi, José Bento |
| 2019 | Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization. Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe |
| 2019 | Traditional and Heavy Tailed Self Regularization in Neural Network Models. Michael W. Mahoney, Charles H. Martin |
| 2019 | Trainable Decoding of Sets of Sequences for Neural Sequence Models. Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra |
| 2019 | Training CNNs with Selective Allocation of Channels. Jongheon Jeong, Jinwoo Shin |
| 2019 | Training Neural Networks with Local Error Signals. Arild Nøkland, Lars Hiller Eidnes |
| 2019 | Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You |
| 2019 | Trajectory-Based Off-Policy Deep Reinforcement Learning. Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel |
| 2019 | Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation. Shani Gamrian, Yoav Goldberg |
| 2019 | Transfer of Samples in Policy Search via Multiple Importance Sampling. Andrea Tirinzoni, Mattia Salvini, Marcello Restelli |
| 2019 | Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang |
| 2019 | Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2019 | Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein |
| 2019 | Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin |
| 2019 | Understanding Geometry of Encoder-Decoder CNNs. Jong Chul Ye, Woon Kyoung Sung |
| 2019 | Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi |
| 2019 | Understanding MCMC Dynamics as Flows on the Wasserstein Space. Chang Liu, Jingwei Zhuo, Jun Zhu |
| 2019 | Understanding Priors in Bayesian Neural Networks at the Unit Level. Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel |
| 2019 | Understanding and Accelerating Particle-Based Variational Inference. Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu |
| 2019 | Understanding and Controlling Memory in Recurrent Neural Networks. Doron Haviv, Alexander Rivkind, Omri Barak |
| 2019 | Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang |
| 2019 | Understanding and correcting pathologies in the training of learned optimizers. Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein |
| 2019 | Understanding the Impact of Entropy on Policy Optimization. Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans |
| 2019 | Understanding the Origins of Bias in Word Embeddings. Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel |
| 2019 | Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension. Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman |
| 2019 | Unifying Orthogonal Monte Carlo Methods. Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller |
| 2019 | Unreproducible Research is Reproducible. Xavier Bouthillier, César Laurent, Pascal Vincent |
| 2019 | Unsupervised Deep Learning by Neighbourhood Discovery. Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu |
| 2019 | Unsupervised Label Noise Modeling and Loss Correction. Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness |
| 2019 | Using Pre-Training Can Improve Model Robustness and Uncertainty. Dan Hendrycks, Kimin Lee, Mantas Mazeika |
| 2019 | Validating Causal Inference Models via Influence Functions. Ahmed M. Alaa, Mihaela van der Schaar |
| 2019 | Variational Annealing of GANs: A Langevin Perspective. Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy V. Bobashev, Lawrence Carin |
| 2019 | Variational Implicit Processes. Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato |
| 2019 | Variational Inference for sparse network reconstruction from count data. Julien Chiquet, Stéphane Robin, Mahendra Mariadassou |
| 2019 | Variational Laplace Autoencoders. Yookoon S. Park, Chris Dongjoo Kim, Gunhee Kim |
| 2019 | Variational Russian Roulette for Deep Bayesian Nonparametrics. Kai Xu, Akash Srivastava, Charles Sutton |
| 2019 | Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration. Vladislav Polianskii, Florian T. Pokorny |
| 2019 | Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban |
| 2019 | Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. Eric Wong, Frank R. Schmidt, J. Zico Kolter |
| 2019 | Wasserstein of Wasserstein Loss for Learning Generative Models. Yonatan Dukler, Wuchen Li, Alex Tong Lin, Guido Montúfar |
| 2019 | Weak Detection of Signal in the Spiked Wigner Model. Hye Won Chung, Ji Oon Lee |
| 2019 | Weakly-Supervised Temporal Localization via Occurrence Count Learning. Julien Schroeter, Kirill A. Sidorov, A. David Marshall |
| 2019 | What is the Effect of Importance Weighting in Deep Learning? Jonathon Byrd, Zachary Chase Lipton |
| 2019 | When Samples Are Strategically Selected. Hanrui Zhang, Yu Cheng, Vincent Conitzer |
| 2019 | White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Hervé Jégou |
| 2019 | Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. Alon Brutzkus, Amir Globerson |
| 2019 | Width Provably Matters in Optimization for Deep Linear Neural Networks. Simon S. Du, Wei Hu |
| 2019 | Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance. Cong Xie, Sanmi Koyejo, Indranil Gupta |
| 2019 | Zero-Shot Knowledge Distillation in Deep Networks. Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty |
| 2019 | kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection. Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert |