Table of Contents

• AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEsGabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause 0001, Bernhard Schölkopf, Stefan Bauer. 1-10 [doi]
• Dynamic Weights in Multi-Objective Deep Reinforcement LearningAxel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher. 11-20 [doi]
• MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood MixingSami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan. 21-29 [doi]
• Communication-Constrained Inference and the Role of Shared RandomnessJayadev Acharya, Clément L. Canonne, Himanshu Tyagi. 30-39 [doi]
• Distributed Learning with Sublinear CommunicationJayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan. 40-50 [doi]
• Communication Complexity in Locally Private Distribution Estimation and Heavy HittersJayadev Acharya, Ziteng Sun. 51-60 [doi]
• Learning Models from Data with Measurement Error: Tackling UnderreportingRoy Adams, Yuelong Ji, XiaoBin Wang, Suchi Saria. 61-70 [doi]
• TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement LearningTameem Adel, Adrian Weller. 71-81 [doi]
• PAC Learnability of Node Functions in Networked Dynamical SystemsAbhijin Adiga, Chris J. Kuhlman, Madhav Marathe, S. S. Ravi, Anil Vullikanti. 82-91 [doi]
• Static Automatic Batching In TensorFlowAshish Agarwal. 92-101 [doi]
• Efficient Full-Matrix Adaptive RegularizationNaman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang. 102-110 [doi]
• Online Control with Adversarial DisturbancesNaman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh. 111-119 [doi]
• Fair Regression: Quantitative Definitions and Reduction-Based AlgorithmsAlekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu. 120-129 [doi]
• Learning to Generalize from Sparse and Underspecified RewardsRishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi 0002. 130-140 [doi]
• The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High DimensionsRaj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick. 141-150 [doi]
• Understanding the Impact of Entropy on Policy OptimizationZafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi 0002, Dale Schuurmans. 151-160 [doi]
• Fairwashing: the risk of rationalizationUlrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp. 161-170 [doi]
• Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture SearchYouhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida. 171-180 [doi]
• Projections for Approximate Policy Iteration AlgorithmsRiad Akrour, Joni Pajarinen, Jan Peters 0001, Gerhard Neumann. 181-190 [doi]
• Validating Causal Inference Models via Influence FunctionsAhmed M. Alaa, Mihaela van der Schaar. 191-201 [doi]
• Multi-objective training of Generative Adversarial Networks with multiple discriminatorsIsabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas. 202-211 [doi]
• Graph Element Networks: adaptive, structured computation and memoryFerran Alet, Adarsh Keshav Jeewajee, Maria Bauzá Villalonga, Alberto Rodriguez, Tomás Lozano-Pérez, Leslie Pack Kaelbling. 212-222 [doi]
• Analogies Explained: Towards Understanding Word EmbeddingsCarl Allen, Timothy M. Hospedales. 223-231 [doi]
• Infinite Mixture Prototypes for Few-shot LearningKelsey Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum. 232-241 [doi]
• A Convergence Theory for Deep Learning via Over-ParameterizationZeyuan Allen Zhu, Yuanzhi Li, Zhao Song. 242-252 [doi]
• Asynchronous Batch Bayesian Optimisation with Improved Local PenalisationAhsan S. Alvi, Bin Xin Ru, Jan-Peter Calliess, Stephen J. Roberts, Michael A. Osborne. 253-262 [doi]
• Bounding User Contributions: A Bias-Variance Trade-off in Differential PrivacyKareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii. 263-271 [doi]
• Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value ApproximationMarco Ancona, Cengiz Öztireli, Markus H. Gross. 272-281 [doi]
• Scaling Up Ordinal Embedding: A Landmark ApproachJesse Anderton, Javed A. Aslam. 282-290 [doi]
• Sorting Out Lipschitz Function ApproximationCem Anil, James Lucas, Roger Grosse. 291-301 [doi]
• Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous DataLuigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi. 302-311 [doi]
• Unsupervised Label Noise Modeling and Loss CorrectionEric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness. 312-321 [doi]
• Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural NetworksSanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang. 322-332 [doi]
• Distributed Weighted Matching via Randomized Composable CoresetsSepehr Assadi, MohammadHossein Bateni, Vahab S. Mirrokni. 333-343 [doi]
• Stochastic Gradient Push for Distributed Deep LearningMahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael Rabbat. 344-353 [doi]
• Bayesian Optimization of Composite FunctionsRaul Astudillo, Peter I. Frazier. 354-363 [doi]
• Linear-Complexity Data-Parallel Earth Mover's Distance ApproximationsKubilay Atasu, Thomas Mittelholzer. 364-373 [doi]
• Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCAJordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavkovic. 374-384 [doi]
• Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured DataSergül Aydöre, Bertrand Thirion, Gaël Varoquaux. 385-394 [doi]
• Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behaviorFadhel Ayed, Juho Lee, Francois Caron. 395-404 [doi]
• Scalable Fair ClusteringArturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner. 405-413 [doi]
• Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANsYogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi. 414-423 [doi]
• Provable Guarantees for Gradient-Based Meta-LearningMaria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar. 424-433 [doi]
• Open-ended learning in symmetric zero-sum gamesDavid Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki 0001, Julien Pérolat, Max Jaderberg, Thore Graepel. 434-443 [doi]
• Concrete Autoencoders: Differentiable Feature Selection and ReconstructionMuhammed Fatih Balin, Abubakar Abid, James Y. Zou. 444-453 [doi]
• HOList: An Environment for Machine Learning of Higher Order Logic Theorem ProvingKshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox. 454-463 [doi]
• Structured agents for physical constructionVictor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter Battaglia, Jessica B. Hamrick. 464-474 [doi]
• Learning to Route in Similarity GraphsDmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko. 475-484 [doi]
• A Personalized Affective Memory Model for Improving Emotion RecognitionPablo V. A. Barros, German Ignacio Parisi, Stefan Wermter. 485-494 [doi]
• Scale-free adaptive planning for deterministic dynamics & discounted rewardsPeter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko. 495-504 [doi]
• Pareto Optimal Streaming Unsupervised ClassificationSoumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai. 505-514 [doi]
• Categorical Feature Compression via Submodular OptimizationMohammadHossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh. 515-523 [doi]
• Noise2Self: Blind Denoising by Self-SupervisionJoshua Batson, Loïc Royer. 524-533 [doi]
• Efficient optimization of loops and limits with randomized telescoping sumsAlex Beatson, Ryan P. Adams. 534-543 [doi]
• Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature SpacesPhilipp Becker, Harit Pandya, Gregor H. W. Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann. 544-552 [doi]
• Switching Linear Dynamics for Variational Bayes FilteringPhilip Becker-Ehmck, Jan Peters 0001, Patrick van der Smagt. 553-562 [doi]
• Active Learning for Probabilistic Structured Prediction of Cuts and MatchingsSima Behpour, Anqi Liu, Brian D. Ziebart. 563-572 [doi]
• Invertible Residual NetworksJens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen. 573-582 [doi]
• Greedy Layerwise Learning Can Scale To ImageNetEugene Belilovsky, Michael Eickenberg, Edouard Oyallon. 583-593 [doi]
• Overcoming Multi-model ForgettingYassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat. 594-603 [doi]
• Optimal Kronecker-Sum Approximation of Real Time Recurrent LearningFrederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger. 604-613 [doi]
• Adversarially Learned Representations for Information Obfuscation and InferenceMartín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, Guillermo Sapiro. 614-623 [doi]
• Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable CaseAlina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang. 624-633 [doi]
• Analyzing Federated Learning through an Adversarial LensArjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo. 634-643 [doi]
• Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field InferenceYatao Bian, Joachim M. Buhmann, Andreas Krause 0001. 644-653 [doi]
• More Efficient Off-Policy Evaluation through Regularized Targeted LearningAurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan. 654-663 [doi]
• A Kernel Perspective for Regularizing Deep Neural NetworksAlberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal. 664-674 [doi]
• Rethinking Lossy Compression: The Rate-Distortion-Perception TradeoffYochai Blau, Tomer Michaeli. 675-685 [doi]
• Correlated bandits or: How to minimize mean-squared error onlineVinay Praneeth Boda, Prashanth L. A.. 686-694 [doi]
• Adversarial Attacks on Node Embeddings via Graph PoisoningAleksandar Bojchevski, Stephan Günnemann. 695-704 [doi]
• Online Variance Reduction with MixturesZalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause 0001. 705-714 [doi]
• Compositional Fairness Constraints for Graph EmbeddingsAvishek Joey Bose, William Hamilton. 715-724 [doi]
• Unreproducible Research is ReproducibleXavier Bouthillier, César Laurent, Pascal Vincent. 725-734 [doi]
• Blended Conditonal GradientsGábor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright. 735-743 [doi]
• Coresets for Ordered Weighted ClusteringVladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu. 744-753 [doi]
• Target Tracking for Contextual Bandits: Application to Demand Side ManagementMargaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz. 754-763 [doi]
• Active Manifolds: A non-linear analogue to Active SubspacesRobert A. Bridges, Anthony D. Gruber, Christopher Felder, Miki E. Verma, Chelsey Hoff. 764-772 [doi]
• Conditioning by adaptive sampling for robust designDavid H. Brookes, Hahnbeom Park, Jennifer Listgarten. 773-782 [doi]
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from ObservationsDaniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum. 783-792 [doi]
• Deep Counterfactual Regret MinimizationNoam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm. 793-802 [doi]
• Understanding the Origins of Bias in Word EmbeddingsMarc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel. 803-811 [doi]
• Low Latency Privacy Preserving InferenceAlon Brutzkus, Ran Gilad-Bachrach, Oren Elisha. 812-821 [doi]
• Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR ProblemAlon Brutzkus, Amir Globerson. 822-830 [doi]
• Adversarial examples from computational constraintsSébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn. 831-840 [doi]
• Self-similar Epochs: Value in arrangementEliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias. 841-850 [doi]
• Learning Generative Models across Incomparable SpacesCharlotte Bunne, David Alvarez-Melis, Andreas Krause 0001, Stefanie Jegelka. 851-861 [doi]
• Rates of Convergence for Sparse Variational Gaussian Process RegressionDavid R. Burt, Carl Edward Rasmussen, Mark van der Wilk. 862-871 [doi]
• What is the Effect of Importance Weighting in Deep Learning?Jonathon Byrd, Zachary Chase Lipton. 872-881 [doi]
• A Quantitative Analysis of the Effect of Batch Normalization on Gradient DescentYongqiang Cai, Qianxiao Li, Zuowei Shen. 882-890 [doi]
• Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein DistancesBugra Can, Mert Gürbüzbalaban, Lingjiong Zhu. 891-901 [doi]
• Active Embedding Search via Noisy Paired ComparisonsGregory Canal, Andrew K. Massimino, Mark A. Davenport, Christopher J. Rozell. 902-911 [doi]
• Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit ProblemJunyu Cao, Wei Sun. 912-920 [doi]
• Competing Against Nash Equilibria in Adversarially Changing Zero-Sum GamesAdrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu. 921-930 [doi]
• Automated Model Selection with Bayesian QuadratureHenry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett. 931-940 [doi]
• Learning Action Representations for Reinforcement LearningYash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip S. Thomas. 941-950 [doi]
• Dynamic Measurement Scheduling for Event Forecasting using Deep RLChun-Hao Chang, Mingjie Mai, Anna Goldenberg. 951-960 [doi]
• On Symmetric Losses for Learning from Corrupted LabelsNontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama. 961-970 [doi]
• Online learning with kernel lossesNiladri S. Chatterji, Aldo Pacchiano, Peter Bartlett. 971-980 [doi]
• Neural Network Attributions: A Causal PerspectiveAditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian. 981-990 [doi]
• PAC Identification of Many Good Arms in Stochastic Multi-Armed BanditsArghya Roy Chaudhuri, Shivaram Kalyanakrishnan. 991-1000 [doi]
• Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesGeorge Chen. 1001-1010 [doi]
• Stein Point Markov Chain Monte CarloWilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates. 1011-1021 [doi]
• Particle Flow Bayes' RuleXinshi Chen, Hanjun Dai, Le Song. 1022-1031 [doi]
• Proportionally Fair ClusteringXingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala. 1032-1041 [doi]
• Information-Theoretic Considerations in Batch Reinforcement LearningJinglin Chen, Nan Jiang. 1042-1051 [doi]
• Generative Adversarial User Model for Reinforcement Learning Based Recommendation SystemXinshi Chen, Shuang Li 0002, Hui Li, Shaohua Jiang, Yuan Qi, Le Song. 1052-1061 [doi]
• Understanding and Utilizing Deep Neural Networks Trained with Noisy LabelsPengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang. 1062-1070 [doi]
• A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein MinimizationYucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu 0001, Jian Peng 0001. 1071-1080 [doi]
• Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain AdaptationXinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang. 1081-1090 [doi]
• Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsPin-Yu Chen, Lingfei Wu, Sijia Liu 0001, Indika Rajapakse. 1091-1101 [doi]
• Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition NumberZaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang. 1102-1111 [doi]
• Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution MatchingZiliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin. 1112-1121 [doi]
• Robust Decision Trees Against Adversarial ExamplesHongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh. 1122-1131 [doi]
• RaFM: Rank-Aware Factorization MachinesXiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, JunZhou Huang. 1132-1140 [doi]
• Control Regularization for Reduced Variance Reinforcement LearningRichard Cheng, Abhinav Verma, Gábor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick. 1141-1150 [doi]
• Predictor-Corrector Policy OptimizationChing-An Cheng, Xinyan Yan, Nathan D. Ratliff, Byron Boots. 1151-1161 [doi]
• Variational Inference for sparse network reconstruction from count dataJulien Chiquet, Stéphane Robin, Mahendra Mariadassou. 1162-1171 [doi]
• Random Walks on Hypergraphs with Edge-Dependent Vertex WeightsUthsav Chitra, Benjamin J. Raphael. 1172-1181 [doi]
• Neural Joint Source-Channel CodingKristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon. 1182-1192 [doi]
• Beyond Backprop: Online Alternating Minimization with Auxiliary VariablesAnna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf. 1193-1202 [doi]
• Unifying Orthogonal Monte Carlo MethodsKrzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller. 1203-1212 [doi]
• Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement LearningCasey Chu, Jose H. Blanchet, Peter W. Glynn. 1213-1222 [doi]
• MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive SummarizationEric Chu, Peter J. Liu. 1223-1232 [doi]
• Weak Detection of Signal in the Spiked Wigner ModelHye Won Chung, Ji Oon Lee. 1233-1241 [doi]
• New results on information theoretic clusteringFerdinando Cicalese, Eduardo Sany Laber, Lucas Murtinho. 1242-1251 [doi]
• Sensitivity Analysis of Linear Structural Causal ModelsCarlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim. 1252-1261 [doi]
• Dimensionality Reduction for Tukey RegressionKenneth L. Clarkson, Ruosong Wang, David P. Woodruff. 1262-1271 [doi]
• On Medians of (Randomized) Pairwise MeansStéphan Clémençon, Pierre Laforgue, Patrice Bertail. 1272-1281 [doi]
• Quantifying Generalization in Reinforcement LearningKarl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman. 1282-1289 [doi]
• Empirical Analysis of Beam Search Performance Degradation in Neural Sequence ModelsEldan Cohen, J. Christopher Beck. 1290-1299 [doi]
• Learning Linear-Quadratic Regulators Efficiently with only √T RegretAlon Cohen, Tomer Koren, Yishay Mansour. 1300-1309 [doi]
• Certified Adversarial Robustness via Randomized SmoothingJeremy M. Cohen, Elan Rosenfeld, J. Zico Kolter. 1310-1320 [doi]
• Gauge Equivariant Convolutional Networks and the Icosahedral CNNTaco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling. 1321-1330 [doi]
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani. 1331-1340 [doi]
• A fully differentiable beam search decoderRonan Collobert, Awni Hannun, Gabriel Synnaeve. 1341-1350 [doi]
• Scalable Metropolis-Hastings for Exact Bayesian Inference with Large DatasetsRobert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet. 1351-1360 [doi]
• Adjustment Criteria for Generalizing Experimental FindingsJuan D. Correa, Jin Tian 0001, Elias Bareinboim. 1361-1369 [doi]
• Online Learning with Sleeping Experts and Feedback GraphsCorinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang. 1370-1378 [doi]
• Active Learning with Disagreement GraphsCorinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile. 1379-1387 [doi]
• Shape Constraints for Set FunctionsAndrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Wang, Tao Zhu. 1388-1396 [doi]
• Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent ConstraintsAndrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You. 1397-1405 [doi]
• Monge blunts Bayes: Hardness Results for Adversarial TrainingZac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder. 1406-1415 [doi]
• Boosted Density Estimation RemasteredZac Cranko, Richard Nock. 1416-1425 [doi]
• Submodular Cost Submodular Cover with an Approximate OracleVictoria G. Crawford, Alan Kuhnle, My T. Thai. 1426-1435 [doi]
• Flexibly Fair Representation Learning by DisentanglementElliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel. 1436-1445 [doi]
• Anytime Online-to-Batch, Optimism and AccelerationAshok Cutkosky. 1446-1454 [doi]
• Matrix-Free Preconditioning in Online LearningAshok Cutkosky, Tamás Sarlós. 1455-1464 [doi]
• Minimal Achievable Sufficient Statistic LearningMilan Cvitkovic, Günther Koliander. 1465-1474 [doi]
• Open Vocabulary Learning on Source Code with a Graph-Structured CacheMilan Cvitkovic, Badal Singh, Animashree Anandkumar. 1475-1485 [doi]
• The Value Function Polytope in Reinforcement LearningRobert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans. 1486-1495 [doi]
• Bayesian Optimization Meets Bayesian Optimal StoppingZhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet. 1496-1506 [doi]
• Policy Certificates: Towards Accountable Reinforcement LearningChristoph Dann, Lihong Li 0001, Wei Wei, Emma Brunskill. 1507-1516 [doi]
• Learning Fast Algorithms for Linear Transforms Using Butterfly FactorizationsTri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré. 1517-1527 [doi]
• A Kernel Theory of Modern Data AugmentationTri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré. 1528-1537 [doi]
• TarMAC: Targeted Multi-Agent CommunicationAbhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau. 1538-1546 [doi]
• Teaching a black-box learnerSanjoy Dasgupta, Daniel Hsu 0001, Stefanos Poulis, Xiaojin Zhu. 1547-1555 [doi]
• Stochastic Deep NetworksGwendoline de Bie, Gabriel Peyré, Marco Cuturi. 1556-1565 [doi]
• Learning-to-Learn Stochastic Gradient Descent with Biased RegularizationGiulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil. 1566-1575 [doi]
• A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer BiologyOnur Dereli, Ceyda Oguz, Mehmet Gönen. 1576-1585 [doi]
• Learning to Convolve: A Generalized Weight-Tying ApproachNichita Diaconu, Daniel E. Worrall. 1586-1595 [doi]
• Sever: A Robust Meta-Algorithm for Stochastic OptimizationIlias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li 0001, Jacob Steinhardt, Alistair Stewart. 1596-1606 [doi]
• Approximated Oracle Filter Pruning for Destructive CNN Width OptimizationXiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan. 1607-1616 [doi]
• Noisy Dual Principal Component PursuitTianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal. 1617-1625 [doi]
• Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement LearningThinh T. Doan, Siva Theja Maguluri, Justin Romberg. 1626-1635 [doi]
• Trajectory-Based Off-Policy Deep Reinforcement LearningAndreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel. 1636-1645 [doi]
• Generalized No Free Lunch Theorem for Adversarial RobustnessElvis Dohmatob. 1646-1654 [doi]
• Width Provably Matters in Optimization for Deep Linear Neural NetworksSimon S. Du, Wei Hu. 1655-1664 [doi]
• Provably efficient RL with Rich Observations via Latent State DecodingSimon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford 0001. 1665-1674 [doi]
• Gradient Descent Finds Global Minima of Deep Neural NetworksSimon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang 0001, Xiyu Zhai. 1675-1685 [doi]
• Incorporating Grouping Information into Bayesian Decision Tree EnsemblesJunliang Du, Antonio R. Linero. 1686-1695 [doi]
• Task-Agnostic Dynamics Priors for Deep Reinforcement LearningYilun Du, Karthik Narasimhan. 1696-1705 [doi]
• Optimal Auctions through Deep LearningPaul Duetting, Zhe Feng 0004, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath. 1706-1715 [doi]
• Wasserstein of Wasserstein Loss for Learning Generative ModelsYonatan Dukler, Wuchen Li, Alex Lin, Guido Montúfar. 1716-1725 [doi]
• Learning interpretable continuous-time models of latent stochastic dynamical systemsLea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani. 1726-1734 [doi]
• Autoregressive Energy MachinesConor Durkan, Charlie Nash. 1735-1744 [doi]
• Band-limited Training and Inference for Convolutional Neural NetworksAdam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin. 1745-1754 [doi]
• Imitating Latent Policies from ObservationAshley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell. 1755-1763 [doi]
• Semi-Cyclic Stochastic Gradient DescentHubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar. 1764-1773 [doi]
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• Analyzing and Improving Representations with the Soft Nearest Neighbor LossNicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton. 2012-2020 [doi]
• Diagnosing Bottlenecks in Deep Q-learning AlgorithmsJustin Fu, Aviral Kumar, Matthew Soh, Sergey Levine. 2021-2030 [doi]
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• Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity RatioKaito Fujii, Shinsaku Sakaue. 2042-2051 [doi]
• Off-Policy Deep Reinforcement Learning without ExplorationScott Fujimoto, David Meger, Doina Precup. 2052-2062 [doi]
• Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationShani Gamrian, Yoav Goldberg. 2063-2072 [doi]
• Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearitiesOctavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn. 2073-2082 [doi]
• Graph U-NetsHongyang Gao, Shuiwang Ji. 2083-2092 [doi]
• Deep Generative Learning via Variational Gradient FlowYuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang. 2093-2101 [doi]
• Rate Distortion For Model Compression: From Theory To PracticeWeihao Gao, Yu-Han Liu, Chong Wang 0002, Sewoong Oh. 2102-2111 [doi]
• Demystifying DropoutHongchang Gao, Jian Pei, Heng Huang. 2112-2121 [doi]
• Geometric Scattering for Graph Data AnalysisFeng Gao, Guy Wolf, Matthew J. Hirn. 2122-2131 [doi]
• Multi-Frequency Phase SynchronizationTingran Gao, Zhizhen Zhao. 2132-2141 [doi]
• Optimal Mini-Batch and Step Sizes for SAGANidham Gazagnadou, Robert M. Gower, Joseph Salmon. 2142-2150 [doi]
• SelectiveNet: A Deep Neural Network with an Integrated Reject OptionYonatan Geifman, Ran El-Yaniv. 2151-2159 [doi]
• A Theory of Regularized Markov Decision ProcessesMatthieu Geist, Bruno Scherrer, Olivier Pietquin. 2160-2169 [doi]
• DeepMDP: Learning Continuous Latent Space Models for Representation LearningCarles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare. 2170-2179 [doi]
• Partially Linear Additive Gaussian Graphical ModelsSinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo. 2180-2190 [doi]
• Learning and Data Selection in Big DatasetsHossein Shokri Ghadikolaei, Hadi G. Ghauch, Carlo Fischione, Mikael Skoglund. 2191-2200 [doi]
• Improved Parallel Algorithms for Density-Based Network ClusteringMohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrovic. 2201-2210 [doi]
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• An Instability in Variational Inference for Topic ModelsBehrooz Ghorbani, Hamid Javadi, Andrea Montanari. 2221-2231 [doi]
• An Investigation into Neural Net Optimization via Hessian Eigenvalue DensityBehrooz Ghorbani, Shankar Krishnan, Ying Xiao. 2232-2241 [doi]
• Data Shapley: Equitable Valuation of Data for Machine LearningAmirata Ghorbani, James Y. Zou. 2242-2251 [doi]
• Efficient Dictionary Learning with Gradient DescentDar Gilboa, Sam Buchanan, John Wright 0001. 2252-2259 [doi]
• A Tree-Based Method for Fast Repeated Sampling of Determinantal Point ProcessesJennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii. 2260-2268 [doi]
• Learning to Groove with Inverse Sequence TransformationsJon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman. 2269-2279 [doi]
• Adversarial Examples Are a Natural Consequence of Test Error in NoiseJustin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk. 2280-2289 [doi]
• Discovering Conditionally Salient Features with Statistical GuaranteesJaime Roquero Gimenez, James Y. Zou. 2290-2298 [doi]
• Estimating Information Flow in Deep Neural NetworksZiv Goldfeld, Ewout van den Berg, Kristjan H. Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy. 2299-2308 [doi]
• Amortized Monte Carlo IntegrationAdam Golinski, Frank Wood, Tom Rainforth. 2309-2318 [doi]
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• The information-theoretic value of unlabeled data in semi-supervised learningAlexander Golovnev, Dávid Pál, Balázs Szörényi. 2328-2336 [doi]
• Efficient Training of BERT by Progressively StackingLinyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu. 2337-2346 [doi]
• Quantile Stein Variational Gradient Descent for Batch Bayesian OptimizationChengYue Gong, Jian Peng 0001, Qiang Liu. 2347-2356 [doi]
• Obtaining Fairness using Optimal Transport TheoryPaula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes. 2357-2365 [doi]
• Combining parametric and nonparametric models for off-policy evaluationOmer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez. 2366-2375 [doi]
• Counterfactual Visual ExplanationsYash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee. 2376-2384 [doi]
• Adaptive Sensor Placement for Continuous SpacesJames A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote. 2385-2393 [doi]
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• Graphite: Iterative Generative Modeling of GraphsAditya Grover, Aaron Zweig, Stefano Ermon. 2434-2444 [doi]
• Fast Algorithm for Generalized Multinomial Models with Ranking DataJiaqi Gu, Guosheng Yin. 2445-2453 [doi]
• Towards a Deep and Unified Understanding of Deep Neural Models in NLPChaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie 0001. 2454-2463 [doi]
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• Simple Black-box Adversarial AttacksChuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger. 2484-2493 [doi]
• Exploring interpretable LSTM neural networks over multi-variable dataTian Guo 0002, Tao Lin, Nino Antulov-Fantulin. 2494-2504 [doi]
• Learning to Exploit Long-term Relational Dependencies in Knowledge GraphsLingbing Guo, Zequn Sun, Wei Hu. 2505-2514 [doi]
• Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded ApplicationsAlbert Gural, Boris Murmann. 2515-2524 [doi]
• IMEXnet A Forward Stable Deep Neural NetworkEldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto. 2525-2534 [doi]
• On The Power of Curriculum Learning in Training Deep NetworksGuy Hacohen, Daphna Weinshall. 2535-2544 [doi]
• Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationFarzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe. 2545-2554 [doi]
• Learning Latent Dynamics for Planning from PixelsDanijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson. 2555-2565 [doi]
• Neural Separation of Observed and Unobserved DistributionsTavi Halperin, Ariel Ephrat, Yedid Hoshen. 2566-2575 [doi]
• Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AILei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang. 2576-2585 [doi]
• Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement LearningSeungyul Han, Youngchul Sung. 2586-2595 [doi]
• Complexity of Linear Regions in Deep NetworksBoris Hanin, David Rolnick. 2596-2604 [doi]
• Importance Sampling Policy Evaluation with an Estimated Behavior PolicyJosiah Hanna, Scott Niekum, Peter Stone. 2605-2613 [doi]
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• Submodular Observation Selection and Information Gathering for Quadratic ModelsAbolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu. 2653-2662 [doi]
• Understanding and Controlling Memory in Recurrent Neural NetworksDoron Haviv, Alexander Rivkind, Omri Barak. 2663-2671 [doi]
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• Faster Algorithms for Binary Matrix FactorizationRavi Kumar 0001, Rina Panigrahy, Ali Rahimi, David P. Woodruff. 3551-3559 [doi]
• Loss Landscapes of Regularized Linear AutoencodersDaniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed. 3560-3569 [doi]
• Geometry and Symmetry in Short-and-Sparse DeconvolutionHan-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright 0001. 3570-3580 [doi]
• A Large-Scale Study on Regularization and Normalization in GANsKarol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly. 3581-3590 [doi]
• Making Decisions that Reduce Discriminatory ImpactsMatt J. Kusner, Chris Russell 0001, Joshua R. Loftus, Ricardo Silva. 3591-3600 [doi]
• Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed BanditsBranislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh. 3601-3610 [doi]
• Characterizing Well-Behaved vs. Pathological Deep Neural NetworksAntoine Labatie. 3611-3621 [doi]
• State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden RepresentationsAlex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer. 3622-3631 [doi]
• A Recurrent Neural Cascade-based Model for Continuous-Time DiffusionSylvain Lamprier. 3632-3641 [doi]
• Projection onto Minkowski Sums with Application to Constrained LearningJoong-Ho Won, Jason Xu, Kenneth Lange. 3642-3651 [doi]
• Safe Policy Improvement with Baseline BootstrappingRomain Laroche, Paul Trichelair, Remi Tachet des Combes. 3652-3661 [doi]
• A Better k-means++ Algorithm via Local SearchSilvio Lattanzi, Christian Sohler. 3662-3671 [doi]
• Lorentzian Distance Learning for Hyperbolic RepresentationsMarc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel. 3672-3681 [doi]
• DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency StructuresAndrew R. Lawrence, Carl Henrik Ek, Neill W. Campbell. 3682-3691 [doi]
• POLITEX: Regret Bounds for Policy Iteration using Expert Prediction3692-3702 [doi]
• Batch Policy Learning under ConstraintsHoang Minh Le 0002, Cameron Voloshin, Yisong Yue. 3703-3712 [doi]
• Target-Based Temporal-Difference LearningDonghwan Lee 0002, Niao He. 3713-3722 [doi]
• Functional Transparency for Structured Data: a Game-Theoretic ApproachGuang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola. 3723-3733 [doi]
• Self-Attention Graph PoolingJunhyun Lee, Inyeop Lee, Jaewoo Kang. 3734-3743 [doi]
• Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksJuho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh. 3744-3753 [doi]
• First-Order Algorithms Converge Faster than $O(1/k)$ on Convex ProblemsChing-Pei Lee, Stephen Wright. 3754-3762 [doi]
• Robust Inference via Generative Classifiers for Handling Noisy LabelsKimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin. 3763-3772 [doi]
• Sublinear Time Nearest Neighbor Search over Generalized Weighted SpaceYifan Lei, Qiang Huang, Mohan S. Kankanhalli, Anthony K. H. Tung. 3773-3781 [doi]
• MONK Outlier-Robust Mean Embedding Estimation by Median-of-MeansMatthieu Lerasle, Zoltán Szabó, Timothée Mathieu, Guillaume Lecué. 3782-3793 [doi]
• Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary GroupMario Lezcano Casado, David Martínez-Rubio. 3794-3803 [doi]
• Are Generative Classifiers More Robust to Adversarial Attacks?Yingzhen Li, John Bradshaw, Yash Sharma. 3804-3814 [doi]
• Sublinear quantum algorithms for training linear and kernel-based classifiersTongyang Li, Shouvanik Chakrabarti, Xiaodi Wu. 3815-3824 [doi]
• LGM-Net: Learning to Generate Matching Networks for Few-Shot LearningHuai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu. 3825-3834 [doi]
• Graph Matching Networks for Learning the Similarity of Graph Structured ObjectsYujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli. 3835-3845 [doi]
• Area AttentionYang Li, Lukasz Kaiser, Samy Bengio, Si Si. 3846-3855 [doi]
• Online Learning to Rank with FeaturesShuai Li, Tor Lattimore, Csaba Szepesvári. 3856-3865 [doi]
• NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural NetworksYandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong. 3866-3876 [doi]
• Bayesian Joint Spike-and-Slab Graphical LassoZehang Li, Tyler H. McCormick, Samuel J. Clark. 3877-3885 [doi]
• Exploiting Worker Correlation for Label Aggregation in CrowdsourcingYuan Li, Benjamin I. P. Rubinstein, Trevor Cohn. 3886-3895 [doi]
• Adversarial camera stickers: A physical camera-based attack on deep learning systemsJuncheng Li, Frank R. Schmidt, J. Zico Kolter. 3896-3904 [doi]
• Towards a Unified Analysis of Random Fourier FeaturesZhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic. 3905-3914 [doi]
• Feature-Critic Networks for Heterogeneous Domain GeneralizationYiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales. 3915-3924 [doi]
• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic ForgettingXilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong. 3925-3934 [doi]
• Alternating Minimizations Converge to Second-Order Optimal SolutionsQiuwei Li, Zhihui Zhu, Gongguo Tang. 3935-3943 [doi]
• Cautious Regret Minimization: Online Optimization with Long-Term Budget ConstraintsNikolaos Liakopoulos, Apostolos Destounis, Georgios S. Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos. 3944-3952 [doi]
• Regularization in directable environments with application to TetrisJan Malte Lichtenberg, Özgür Simsek. 3953-3962 [doi]
• Inference and Sampling of $K_33$-free Ising ModelsValerii Likhosherstov, Yury Maximov, Misha Chertkov. 3963-3972 [doi]
• Kernel-Based Reinforcement Learning in Robust Markov Decision ProcessesShiau Hong Lim, Arnaud Autef. 3973-3981 [doi]
• On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent AlgorithmsTianyi Lin, Nhat Ho, Michael I. Jordan. 3982-3991 [doi]
• Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family ApproximationsWu Lin, Mohammad Emtiyaz Khan, Mark W. Schmidt. 3992-4002 [doi]
• Acceleration of SVRG and Katyusha X by Inexact PreconditioningYanli Liu, Fei Feng, Wotao Yin. 4003-4012 [doi]
• Transferable Adversarial Training: A General Approach to Adapting Deep ClassifiersHong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan. 4013-4022 [doi]
• Rao-Blackwellized Stochastic Gradients for Discrete DistributionsRunjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe. 4023-4031 [doi]
• Sparse Extreme Multi-label Learning with Oracle PropertyWeiwei Liu, Xiaobo Shen. 4032-4041 [doi]
• Data Poisoning Attacks on Stochastic BanditsFang Liu, Ness B. Shroff. 4042-4050 [doi]
• The Implicit Fairness Criterion of Unconstrained LearningLydia T. Liu, Max Simchowitz, Moritz Hardt. 4051-4060 [doi]
• Taming MAML: Efficient unbiased meta-reinforcement learningHao Liu, Richard Socher, Caiming Xiong. 4061-4071 [doi]
• On Certifying Non-Uniform Bounds against Adversarial AttacksChen Liu, Ryota Tomioka, Volkan Cevher. 4072-4081 [doi]
• Understanding and Accelerating Particle-Based Variational InferenceChang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu 0001. 4082-4092 [doi]
• Understanding MCMC Dynamics as Flows on the Wasserstein SpaceChang Liu, Jingwei Zhuo, Jun Zhu 0001. 4093-4103 [doi]
• Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and DiffusionsAntoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter. 4104-4113 [doi]
• Challenging Common Assumptions in the Unsupervised Learning of Disentangled RepresentationsFrancesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem. 4114-4124 [doi]
• Bayesian Counterfactual Risk MinimizationBen London, Ted Sandler. 4125-4133 [doi]
• PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex OptimizationSongtao Lu, Mingyi Hong, Zhengdao Wang. 4134-4143 [doi]
• Neurally-Guided Structure InferenceSidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu 0001. 4144-4153 [doi]
• Optimal Algorithms for Lipschitz Bandits with Heavy-tailed RewardsShiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang. 4154-4163 [doi]
• CoT: Cooperative Training for Generative Modeling of Discrete DataSidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang 0001. 4164-4172 [doi]
• Generalized Approximate Survey Propagation for High-Dimensional EstimationCarlo Lucibello, Luca Saglietti, Yue M. Lu. 4173-4182 [doi]
• High-Fidelity Image Generation With Fewer LabelsMario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly. 4183-4192 [doi]
• Leveraging Low-Rank Relations Between Surrogate Tasks in Structured PredictionGiulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto. 4193-4202 [doi]
• Differentiable Dynamic Normalization for Learning Deep RepresentationLuo Ping, Zhanglin Peng, Wenqi Shao, Ruimao Zhang, Jiamin Ren, Lingyun Wu. 4203-4211 [doi]
• Disentangled Graph Convolutional NetworksJianxin Ma, Peng Cui 0001, Kun Kuang, Xin Wang, Wenwu Zhu 0001. 4212-4221 [doi]
• Variational Implicit ProcessesChao Ma, Yingzhen Li, José Miguel Hernández-Lobato. 4222-4233 [doi]
• EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAEChao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang. 4234-4243 [doi]
• Bayesian leave-one-out cross-validation for large dataMåns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari. 4244-4253 [doi]
• Composable Core-sets for Determinant Maximization: A Simple Near-Optimal AlgorithmSepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei. 4254-4263 [doi]
• Guided evolutionary strategies: augmenting random search with surrogate gradientsNiru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein. 4264-4273 [doi]
• Data Poisoning Attacks in Multi-Party LearningSaeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed. 4274-4283 [doi]
• Traditional and Heavy Tailed Self Regularization in Neural Network ModelsMichael Mahoney, Charles Martin. 4284-4293 [doi]
• Curvature-Exploiting Acceleration of Elastic Net ComputationsVien V. Mai, Mikael Johansson. 4294-4303 [doi]
• Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient AlgorithmsAshok Vardhan Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh. 4304-4313 [doi]
• Calibrated Model-Based Deep Reinforcement LearningAli Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon. 4314-4323 [doi]
• Learning from Delayed Outcomes via Proxies with Applications to Recommender SystemsTimothy Arthur Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan. 4324-4332 [doi]
• Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor ModelsStefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová. 4333-4342 [doi]
• A Baseline for Any Order Gradient Estimation in Stochastic Computation GraphsJingkai Mao, Jakob N. Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson. 4343-4351 [doi]
• Adversarial Generation of Time-Frequency Features with application in audio synthesisAndrés Marafioti, Nathanaël Perraudin, Nicki Holighaus, Piotr Majdak. 4352-4362 [doi]
• On the Universality of Invariant NetworksHaggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman. 4363-4371 [doi]
• Decomposing feature-level variation with Covariate Gaussian Process Latent Variable ModelsKaspar Märtens, Kieran R. Campbell, Christopher Yau. 4372-4381 [doi]
• Fairness-Aware Learning for Continuous Attributes and TreatmentsJérémie Mary, Clément Calauzènes, Noureddine El Karoui. 4382-4391 [doi]
• Optimal Minimal Margin Maximization with BoostingAlexander Mathiasen, Kasper Green Larsen, Allan Grønlund. 4392-4401 [doi]
• Disentangling Disentanglement in Variational AutoencodersEmile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh. 4402-4412 [doi]
• MIWAE: Deep Generative Modelling and Imputation of Incomplete Data SetsPierre-Alexandre Mattei, Jes Frellsen. 4413-4423 [doi]
• Distributional Reinforcement Learning for Efficient ExplorationBorislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu. 4424-4434 [doi]
• Graphical-model based estimation and inference for differential privacyRyan Mckenna, Daniel Sheldon, Gerome Miklau. 4435-4444 [doi]
• Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical SystemsGeoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds. 4445-4455 [doi]
• Toward Controlling Discrimination in Online Ad AuctionsAnay Mehrotra, L. Elisa Celis, Nisheeth K. Vishnoi. 4456-4465 [doi]
• Stochastic Blockmodels meet Graph Neural NetworksNikhil Mehta, Lawrence Carin, Piyush Rai. 4466-4474 [doi]
• Imputing Missing Events in Continuous-Time Event StreamsHongyuan Mei, Guanghui Qin, Jason Eisner. 4475-4485 [doi]
• Same, Same But Different: Recovering Neural Network Quantization Error Through Weight FactorizationEldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman. 4486-4495 [doi]
• The Wasserstein TransformFacundo Mémoli, Zane T. Smith, Zhengchao Wan. 4496-4504 [doi]
• Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural NetworksCharith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin. 4505-4515 [doi]
• Geometric Losses for Distributional LearningArthur Mensch, Mathieu Blondel, Gabriel Peyré. 4516-4525 [doi]
• Spectral Clustering of Signed Graphs via Matrix Power MeansPedro Mercado, Francesco Tudisco, Matthias Hein 0001. 4526-4536 [doi]
• Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized OptimizationMichael R. Metel, Akiko Takeda. 4537-4545 [doi]
• Reinforcement Learning in Configurable Continuous EnvironmentsAlberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli. 4546-4555 [doi]
• Understanding and correcting pathologies in the training of learned optimizersLuke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein. 4556-4565 [doi]
• Optimality Implies Kernel Sum Classifiers are Statistically EfficientRaphael Arkady Meyer, Jean Honorio. 4566-4574 [doi]
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• Discriminative Regularization for Latent Variable Models with Applications to ElectrocardiographyAndrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan. 4585-4594 [doi]
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• Co-manifold learning with missing dataGal Mishne, Eric C. Chi, Ronald R. Coifman. 4605-4614 [doi]
• Agnostic Federated LearningMehryar Mohri, Gary Sivek, Ananda Theertha Suresh. 4615-4625 [doi]
• Flat Metric Minimization with Applications in Generative ModelingThomas Möllenhoff, Daniel Cremers. 4626-4635 [doi]
• Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial OptimizationSeungyong Moon, Gaon An, Hyun Oh Song. 4636-4645 [doi]
• Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterizationHesham Mostafa, Xin Wang. 4646-4655 [doi]
• A Dynamical Systems Perspective on Nesterov AccelerationMichael Muehlebach, Michael I. Jordan. 4656-4662 [doi]
• Relational Pooling for Graph RepresentationsRyan L. Murphy, Balasubramaniam Srinivasan 0003, Vinayak Rao, Bruno Ribeiro 0001. 4663-4673 [doi]
• Learning Optimal Fair PoliciesRazieh Nabi, Daniel Malinsky, Ilya Shpitser. 4674-4682 [doi]
• Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep ModelsMor Shpigel Nacson, Suriya Gunasekar, Jason Lee, Nathan Srebro, Daniel Soudry. 4683-4692 [doi]
• A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based LearningYoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita 0001, Masanori Koyama. 4693-4702 [doi]
• SGD without Replacement: Sharper Rates for General Smooth Convex FunctionsDheeraj Nagaraj, Prateek Jain 0002, Praneeth Netrapalli. 4703-4711 [doi]
• Dropout as a Structured Shrinkage PriorEric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth. 4712-4722 [doi]
• Hybrid Models with Deep and Invertible FeaturesEric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan. 4723-4732 [doi]
• Learning Context-dependent Label Permutations for Multi-label ClassificationJinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz. 4733-4742 [doi]
• Zero-Shot Knowledge Distillation in Deep NetworksGaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty. 4743-4751 [doi]
• A Framework for Bayesian Optimization in Embedded SubspacesAmin Nayebi, Alexander Munteanu, Matthias Poloczek. 4752-4761 [doi]
• Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless MeasurementsSeyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani. 4762-4770 [doi]
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• Learning to bid in revenue-maximizing auctionsThomas Nedelec, Noureddine El Karoui, Vianney Perchet. 4781-4789 [doi]
• On Connected Sublevel Sets in Deep LearningQuynh Nguyen. 4790-4799 [doi]
• Anomaly Detection With Multiple-Hypotheses PredictionsDuc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox. 4800-4809 [doi]
• Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex OptimizationThanh Huy Nguyen, Umut Simsekli, Gaël Richard. 4810-4819 [doi]
• Rotation Invariant Householder Parameterization for Bayesian PCARajbir-Singh Nirwan, Nils Bertschinger. 4820-4828 [doi]
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• Training Neural Networks with Local Error SignalsArild Nøkland, Lars Hiller Eidnes. 4839-4850 [doi]
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• Learning to Infer Program SketchesMaxwell I. Nye, Luke B. Hewitt, Joshua B. Tenenbaum, Armando Solar-Lezama. 4861-4870 [doi]
• Tensor Variable Elimination for Plated Factor GraphsFritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander M. Rush, Noah Goodman. 4871-4880 [doi]
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsMichael Oberst, David Sontag. 4881-4890 [doi]
• Model Function Based Conditional Gradient Method with Armijo-like Line SearchPeter Ochs, Yura Malitsky. 4891-4900 [doi]
• TensorFuzz: Debugging Neural Networks with Coverage-Guided FuzzingAugustus Odena, Catherine Olsson, David Andersen, Ian J. Goodfellow. 4901-4911 [doi]
• Scalable Learning in Reproducing Kernel Krein SpacesDino Oglic, Thomas Gärtner. 4912-4921 [doi]
• Approximation and non-parametric estimation of ResNet-type convolutional neural networksKenta Oono, Taiji Suzuki. 4922-4931 [doi]
• Orthogonal Random Forest for Causal InferenceMiruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu. 4932-4941 [doi]
• Inferring Heterogeneous Causal Effects in Presence of Spatial ConfoundingMuhammad Osama, Dave Zachariah, Thomas B. Schön. 4942-4950 [doi]
• Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?Samet Oymak, Mahdi Soltanolkotabi. 4951-4960 [doi]
• Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost alwaysIoannis Panageas, Georgios Piliouras, Xiao Wang. 4961-4969 [doi]
• Improving Adversarial Robustness via Promoting Ensemble DiversityTianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu. 4970-4979 [doi]
• Nonparametric Bayesian Deep Networks with Local CompetitionKonstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis. 4980-4988 [doi]
• Optimistic Policy Optimization via Multiple Importance SamplingMatteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli. 4989-4999 [doi]
• Deep Residual Output Layers for Neural Language GenerationNikolaos Pappas, James Henderson. 5000-5011 [doi]
• Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet HessiansVardan Papyan. 5012-5021 [doi]
• Generalized Majorization-MinimizationSobhan Naderi Parizi, Kun He 0003, Reza Aghajani, Stan Sclaroff, Pedro F. Felzenszwalb. 5022-5031 [doi]
• Variational Laplace AutoencodersYookoon S. Park, Chris Dongjoo Kim, Gunhee Kim. 5032-5041 [doi]
• The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical StudyDaniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith. 5042-5051 [doi]
• Spectral Approximate InferenceSejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin. 5052-5061 [doi]
• Self-Supervised Exploration via DisagreementDeepak Pathak, Dhiraj Gandhi, Abhinav Gupta 0001. 5062-5071 [doi]
• Subspace Robust Wasserstein DistancesFrançois-Pierre Paty, Marco Cuturi. 5072-5081 [doi]
• Fingerprint Policy Optimisation for Robust Reinforcement LearningSupratik Paul, Michael A. Osborne, Shimon Whiteson. 5082-5091 [doi]
• COMIC: Multi-view Clustering Without Parameter SelectionXi Peng 0001, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou. 5092-5101 [doi]
• Domain Agnostic Learning with Disentangled RepresentationsXingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko. 5102-5112 [doi]
• Collaborative Channel Pruning for Deep NetworksHanyu Peng, Jiaxiang Wu, Shifeng Chen, JunZhou Huang. 5113-5122 [doi]
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• Cognitive model priors for predicting human decisionsJoshua C. Peterson, David Bourgin, Daniel Reichman 0001, Thomas L. Griffiths, Stuart J. Russell. 5133-5141 [doi]
• Towards Understanding Knowledge DistillationMary Phuong, Christoph Lampert. 5142-5151 [doi]
• Temporal Gaussian Mixture Layer for VideosA. J. Piergiovanni, Michael S. Ryoo. 5152-5161 [doi]
• Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo IntegrationVladislav Polianskii, Florian T. Pokorny. 5162-5170 [doi]
• On Variational Bounds of Mutual InformationBen Poole, Sherjil Ozair, Aäron Van Den Oord, Alex Alemi, George Tucker. 5171-5180 [doi]
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• SAGA with Arbitrary SamplingXun Qian, Zheng Qu, Peter Richtárik. 5190-5199 [doi]
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• AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder LossKaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson. 5210-5219 [doi]
• Fault Tolerance in Iterative-Convergent Machine LearningAurick Qiao, Bryon Aragam, Bingjing Zhang, Eric Xing. 5220-5230 [doi]
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• Nonlinear Distributional Gradient Temporal-Difference LearningChao Qu, Shie Mannor, Huan Xu. 5251-5260 [doi]
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• On the Spectral Bias of Neural NetworksNasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Dräxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville. 5301-5310 [doi]
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• Does Data Augmentation Lead to Positive Margin?Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos. 5321-5330 [doi]
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context VariablesKate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen. 5331-5340 [doi]
• Screening rules for Lasso with non-convex Sparse RegularizersAlain Rakotomamonjy, Gilles Gasso, Joseph Salmon. 5341-5350 [doi]
• Topological Data Analysis of Decision Boundaries with Application to Model SelectionKarthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody. 5351-5360 [doi]
• HyperGAN: A Generative Model for Diverse, Performant Neural NetworksNeale Ratzlaff, Fuxin Li. 5361-5369 [doi]
• Efficient On-Device Models using Neural ProjectionsSujith Ravi. 5370-5379 [doi]
• A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter EstimationRamin Raziperchikolaei, Harish S. Bhat. 5380-5388 [doi]
• Do ImageNet Classifiers Generalize to ImageNet?Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar. 5389-5400 [doi]
• Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label NoiseHenry W. J. Reeve, Ata Kabán. 5401-5409 [doi]
• Almost Unsupervised Text to Speech and Automatic Speech RecognitionYi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu. 5410-5419 [doi]
• Adaptive Antithetic Sampling for Variance ReductionHongyu Ren, Shengjia Zhao, Stefano Ermon. 5420-5428 [doi]
• Adversarial Online Learning with noiseAlon Resler, Yishay Mansour. 5429-5437 [doi]
• A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point ProcessesAlireza Rezaei, Shayan Oveis Gharan. 5438-5447 [doi]
• A Persistent Weisfeiler-Lehman Procedure for Graph ClassificationBastian Rieck, Christian Bock, Karsten M. Borgwardt. 5448-5458 [doi]
• Efficient learning of smooth probability functions from Bernoulli tests with guaranteesPaul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher. 5459-5467 [doi]
• Separable value functions across time-scalesJoshua Romoff, Peter Henderson 0002, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill. 5468-5477 [doi]
• Online Convex Optimization in Adversarial Markov Decision ProcessesAviv Rosenberg, Yishay Mansour. 5478-5486 [doi]
• Good Initializations of Variational Bayes for Deep ModelsSimone Rossi, Pietro Michiardi, Maurizio Filippone. 5487-5497 [doi]
• The Odds are Odd: A Statistical Test for Detecting Adversarial ExamplesKevin Roth, Yannic Kilcher, Thomas Hofmann. 5498-5507 [doi]
• Neuron birth-death dynamics accelerates gradient descent and converges asymptoticallyGrant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden. 5508-5517 [doi]
• Iterative Linearized Control: Stable Algorithms and Complexity GuaranteesVincent Roulet, Dmitriy Drusvyatskiy, Siddhartha Srinivasa, Zaïd Harchaoui. 5518-5527 [doi]
• Statistics and Samples in Distributional Reinforcement LearningMark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney. 5528-5536 [doi]
• A Contrastive Divergence for Combining Variational Inference and MCMCFrancisco Ruiz, Michalis Titsias. 5537-5545 [doi]
• Plug-and-Play Methods Provably Converge with Properly Trained DenoisersErnest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin. 5546-5557 [doi]
• White-box vs Black-box: Bayes Optimal Strategies for Membership InferenceAlexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Herve Jegou. 5558-5567 [doi]
• Tractable n-Metrics for Multiple GraphsSam Safavi, José Bento. 5568-5578 [doi]
• An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping RuleTouqir Sajed, Or Sheffet. 5579-5588 [doi]
• Deep Gaussian Processes with Importance-Weighted Variational InferenceHugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth. 5589-5598 [doi]
• Multivariate Submodular OptimizationRichard Santiago, F. Bruce Shepherd. 5599-5609 [doi]
• Near optimal finite time identification of arbitrary linear dynamical systemsTuhin Sarkar, Alexander Rakhlin. 5610-5618 [doi]
• Breaking Inter-Layer Co-Adaptation by Classifier AnonymizationIkuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka. 5619-5627 [doi]
• A Theoretical Analysis of Contrastive Unsupervised Representation LearningNikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar. 5628-5637 [doi]
• Locally Private Bayesian Inference for Count ModelsAaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach. 5638-5648 [doi]
• Weakly-Supervised Temporal Localization via Occurrence Count LearningJulien Schroeter, Kirill A. Sidorov, A. David Marshall. 5649-5659 [doi]
• Discovering Context Effects from Raw Choice DataArjun Seshadri, Alex Peysakhovich, Johan Ugander. 5660-5669 [doi]
• On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward InferenceRohin Shah, Noah Gundotra, Pieter Abbeel, Anca D. Dragan. 5670-5679 [doi]
• Exploration Conscious Reinforcement Learning RevisitedLior Shani, Yonathan Efroni, Shie Mannor. 5680-5689 [doi]
• Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed DataVatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant. 5690-5700 [doi]
• Conditional Independence in Testing Bayesian NetworksYujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche. 5701-5709 [doi]
• Learning to Clear the MarketWeiran Shen, Sébastien Lahaie, Renato Paes Leme. 5710-5718 [doi]
• Mixture Models for Diverse Machine Translation: Tricks of the TradeTianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato. 5719-5728 [doi]
• Hessian Aided Policy GradientZebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi. 5729-5738 [doi]
• Learning with Bad Training Data via Iterative Trimmed Loss MinimizationYanyao Shen, Sujay Sanghavi. 5739-5748 [doi]
• Replica Conditional Sequential Monte CarloAlex Shestopaloff, Arnaud Doucet. 5749-5757 [doi]
• Scalable Training of Inference Networks for Gaussian-Process ModelsJiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu 0001. 5758-5768 [doi]
• Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active LearningWeishi Shi, Qi Yu 0001. 5769-5778 [doi]
• Model-Based Active ExplorationPranav Shyam, Wojciech Jaskowski, Faustino Gomez. 5779-5788 [doi]
• Rehashing Kernel Evaluation in High DimensionsParis Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis. 5789-5798 [doi]
• Revisiting precision recall definition for generative modelingLoïc Simon, Ryan Webster, Julien Rabin. 5799-5808 [doi]
• First-Order Adversarial Vulnerability of Neural Networks and Input DimensionCarl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz. 5809-5817 [doi]
• Refined Complexity of PCA with OutliersKirill Simonov, Fedor V. Fomin, Petr A. Golovach, Fahad Panolan. 5818-5826 [doi]
• A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural NetworksUmut Simsekli, Levent Sagun, Mert Gürbüzbalaban. 5827-5837 [doi]
• Non-Parametric Priors For Generative Adversarial NetworksRajhans Singh, Pavan K. Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun. 5838-5847 [doi]
• Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning InterpretationSahil Singla, Eric Wallace, Shi Feng, Soheil Feizi. 5848-5856 [doi]
• kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable SelectionLotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert. 5857-5865 [doi]
• GEOMetrics: Exploiting Geometric Structure for Graph-Encoded ObjectsEdward J. Smith, Scott Fujimoto, Adriana Romero, David Meger. 5866-5876 [doi]
• The Evolved TransformerDavid So, Quoc Le, Chen Liang. 5877-5886 [doi]
• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningKyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi. 5887-5896 [doi]
• Distribution calibration for regressionHao Song, Tom Diethe, Meelis Kull, Peter A. Flach. 5897-5906 [doi]
• SELFIE: Refurbishing Unclean Samples for Robust Deep LearningHwanjun Song, Minseok Kim, Jae-Gil Lee 0001. 5907-5915 [doi]
• Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveZhao Song, Ronald Parr, Lawrence Carin. 5916-5925 [doi]
• MASS: Masked Sequence to Sequence Pre-training for Language GenerationKaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu. 5926-5936 [doi]
• Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix MultiplicationPedro Soto, Jun Li, Xiaodi Fan. 5937-5945 [doi]
• Compressing Gradient Optimizers via Count-SketchesRyan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava. 5946-5955 [doi]
• Escaping Saddle Points with Adaptive Gradient MethodsMatthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra. 5956-5965 [doi]
• Faster Attend-Infer-Repeat with Tractable Probabilistic ModelsKarl Stelzner