Table of Contents

• Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control ProblemsMarc Abeille, Alessandro Lazaric. 1-9 [doi]
• State Abstractions for Lifelong Reinforcement LearningDavid Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman. 10-19 [doi]
• Policy and Value Transfer in Lifelong Reinforcement LearningDavid Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael L. Littman. 20-29 [doi]
• INSPECTRE: Privately Estimating the UnseenJayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang. 30-39 [doi]
• Learning Representations and Generative Models for 3D Point CloudsPanos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas. 40-49 [doi]
• Discovering Interpretable Representations for Both Deep Generative and Discriminative ModelsTameem Adel, Zoubin Ghahramani, Adrian Weller. 50-59 [doi]
• A Reductions Approach to Fair ClassificationAlekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford 0001, Hanna M. Wallach. 60-69 [doi]
• Accelerated Spectral RankingArpit Agarwal, Prathamesh Patil, Shivani Agarwal 0001. 70-79 [doi]
• MISSION: Ultra Large-Scale Feature Selection using Count-SketchesAmirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk. 80-88 [doi]
• Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG ModelsRaj Agrawal, Caroline Uhler, Tamara Broderick. 89-98 [doi]
• Proportional Allocation: Simple, Distributed, and Diverse Matching with High EntropyShipra Agrawal 0001, Morteza Zadimoghaddam, Vahab S. Mirrokni. 99-108 [doi]
• Bucket Renormalization for Approximate InferenceSungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin. 109-118 [doi]
• oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor AnalysisSamuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox. 119-128 [doi]
• Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignAhmed M. Alaa, Mihaela van der Schaar. 129-138 [doi]
• AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningAhmed M. Alaa, Mihaela van der Schaar. 139-148 [doi]
• Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale OptimizationIbrahim M. Alabdulmohsin. 149-158 [doi]
• Fixing a Broken ELBOAlexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy 0002. 159-168 [doi]
• Differentially Private Identity and Equivalence Testing of Discrete DistributionsMaryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld. 169-178 [doi]
• Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex OptimizationZeyuan Allen Zhu. 179-185 [doi]
• Make the Minority Great Again: First-Order Regret Bound for Contextual BanditsZeyuan Allen Zhu, Sébastien Bubeck, Yuanzhi Li. 186-194 [doi]
• Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired DataAmjad Almahairi, Sai Rajeshwar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville. 195-204 [doi]
• Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes TheoryRon Amit, Ron Meir. 205-214 [doi]
• MAGAN: Aligning Biological ManifoldsMatthew Amodio, Smita Krishnaswamy. 215-223 [doi]
• Subspace Embedding and Linear Regression with Orlicz NormAlexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong. 224-233 [doi]
• Efficient Gradient-Free Variational Inference using Policy SearchOleg Arenz, Mingjun Zhong, Gerhard Neumann. 234-243 [doi]
• On the Optimization of Deep Networks: Implicit Acceleration by OverparameterizationSanjeev Arora, Nadav Cohen, Elad Hazan. 244-253 [doi]
• Stronger Generalization Bounds for Deep Nets via a Compression ApproachSanjeev Arora, Rong Ge 0001, Behnam Neyshabur, Yi Zhang. 254-263 [doi]
• Lipschitz Continuity in Model-based Reinforcement LearningKavosh Asadi, Dipendra Misra, Michael L. Littman. 264-273 [doi]
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial ExamplesAnish Athalye, Nicholas Carlini, David A. Wagner 0001. 274-283 [doi]
• Synthesizing Robust Adversarial ExamplesAnish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok. 284-293 [doi]
• Clustering Semi-Random Mixtures of GaussiansPranjal Awasthi, Aravindan Vijayaraghavan. 294-303 [doi]
• Contextual Graph Markov Model: A Deep and Generative Approach to Graph ProcessingDavide Bacciu, Federico Errica, Alessio Micheli. 304-313 [doi]
• Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) FunctionsWenruo Bai, Jeffrey A. Bilmes. 314-323 [doi]
• Comparing Dynamics: Deep Neural Networks versus Glassy SystemsMarco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli. 324-333 [doi]
• SMAC: Simultaneous Mapping and Clustering Using Spectral DecompositionsChandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang. 334-343 [doi]
• A Boo(n) for Evaluating Architecture PerformanceOndrej Bajgar, Rudolf Kadlec, Jan Kleindienst. 344-352 [doi]
• Learning to BranchMaria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik. 353-362 [doi]
• The Mechanics of n-Player Differentiable GamesDavid Balduzzi, Sébastien Racaniere, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel. 363-372 [doi]
• Spline Filters For End-to-End Deep LearningRandall Balestriero, Romain Cosentino, Hervé Glotin, Richard G. Baraniuk. 373-382 [doi]
• A Spline Theory of Deep NetworksRandall Balestriero, Richard G. Baraniuk. 383-392 [doi]
• Approximation Guarantees for Adaptive SamplingEric Balkanski, Yaron Singer. 393-402 [doi]
• Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal DenoisingBorja Balle, Yu-Xiang Wang. 403-412 [doi]
• Dissecting Adam: The Sign, Magnitude and Variance of Stochastic GradientsLukas Balles, Philipp Hennig. 413-422 [doi]
• Differentially Private Database Release via Kernel Mean EmbeddingsMatej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf. 423-431 [doi]
• Improving Optimization in Models With Continuous Symmetry BreakingRobert Bamler, Stephan Mandt. 432-441 [doi]
• Improved Training of Generative Adversarial Networks using Representative FeaturesDuhyeon Bang, Hyunjung Shim. 442-451 [doi]
• Using Inherent Structures to design Lean 2-layer RBMsAbhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya. 452-460 [doi]
• Classification from Pairwise Similarity and Unlabeled DataHan Bao, Gang Niu, Masashi Sugiyama. 461-470 [doi]
• Bayesian Optimization of Combinatorial StructuresRicardo Baptista, Matthias Poloczek. 471-480 [doi]
• Geodesic Convolutional Shape OptimizationPierre Baqué, Edoardo Remelli, François Fleuret, Pascal Fua. 481-490 [doi]
• Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision ProblemsEugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt. 491-499 [doi]
• Testing Sparsity over Known and Unknown BasesSiddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal. 500-509 [doi]
• Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy ImprovementAndré Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos. 510-519 [doi]
• Gradient descent with identity initialization efficiently learns positive definite linear transformationsPeter L. Bartlett, David P. Helmbold, Phil Long. 520-529 [doi]
• Mutual Information Neural EstimationMohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville. 530-539 [doi]
• To Understand Deep Learning We Need to Understand Kernel LearningMikhail Belkin, Siyuan Ma, Soumik Mandal. 540-548 [doi]
• Understanding and Simplifying One-Shot Architecture SearchGabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc V. Le. 549-558 [doi]
• SIGNSGD: Compressed Optimisation for Non-Convex ProblemsJeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar. 559-568 [doi]
• Distributed Clustering via LSH Based Data PartitioningAditya Bhaskara, Maheshakya Wijewardena. 569-578 [doi]
• Autoregressive Convolutional Neural Networks for Asynchronous Time SeriesMikolaj Binkowski, Gautier Marti, Philippe Donnat. 579-588 [doi]
• Adaptive Sampled Softmax with Kernel Based SamplingGuy Blanc, Steffen Rendle. 589-598 [doi]
• Optimizing the Latent Space of Generative NetworksPiotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam. 599-608 [doi]
• NetGAN: Generating Graphs via Random WalksAleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann. 609-618 [doi]
• A Progressive Batching L-BFGS Method for Machine LearningRaghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang. 619-628 [doi]
• Prediction Rule ReshapingMatt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty. 629-637 [doi]
• QuantTree: Histograms for Change Detection in Multivariate Data StreamsGiacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò. 638-647 [doi]
• Matrix Norms in Data Streams: Faster, Multi-Pass and Row-OrderVladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li 0002, David P. Woodruff, Lin F. Yang. 648-657 [doi]
• Predict and Constrain: Modeling Cardinality in Deep Structured PredictionNataly Brukhim, Amir Globerson. 658-666 [doi]
• Quasi-Monte Carlo Variational InferenceAlexander Buchholz, Florian Wenzel, Stephan Mandt. 667-676 [doi]
• Path-Level Network Transformation for Efficient Architecture SearchHan Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu. 677-686 [doi]
• Improved Large-Scale Graph Learning through Ridge Spectral SparsificationDaniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko. 687-696 [doi]
• Bayesian Coreset Construction via Greedy Iterative Geodesic AscentTrevor Campbell, Tamara Broderick. 697-705 [doi]
• Adversarial Learning with Local Coordinate CodingJiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, JunZhou Huang, Mingkui Tan. 706-714 [doi]
• Fair and Diverse DPP-Based Data SummarizationL. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande 0001, Tarun Kathuria, Nisheeth K. Vishnoi. 715-724 [doi]
• Conditional Noise-Contrastive Estimation of Unnormalised ModelsCiwan Ceylan, Michael U. Gutmann. 725-733 [doi]
• Adversarial Time-to-Event ModelingPaidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin, Ricardo Henao. 734-743 [doi]
• Stability and Generalization of Learning Algorithms that Converge to Global OptimaZachary B. Charles, Dimitris S. Papailiopoulos. 744-753 [doi]
• Learning and MemorizationSatrajit Chatterjee. 754-762 [doi]
• On the Theory of Variance Reduction for Stochastic Gradient Monte CarloNiladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan. 763-772 [doi]
• Hierarchical Clustering with Structural ConstraintsVaggos Chatziafratis, Rad Niazadeh, Moses Charikar. 773-782 [doi]
• Hierarchical Deep Generative Models for Multi-Rate Multivariate Time SeriesZhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu. 783-792 [doi]
• GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask NetworksZhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich. 793-802 [doi]
• Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?Lin Chen 0003, Moran Feldman, Amin Karbasi. 803-812 [doi]
• Projection-Free Online Optimization with Stochastic Gradient: From Convexity to SubmodularityLin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi. 813-822 [doi]
• Continuous-Time Flows for Efficient Inference and Density EstimationChangyou Chen, Chunyuan Li, Liquan Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin. 823-832 [doi]
• Scalable Bilinear Learning Using State and Action FeaturesYichen Chen, Lihong Li 0001, Mengdi Wang. 833-842 [doi]
• Stein PointsWilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates. 843-852 [doi]
• Learning K-way D-dimensional Discrete Codes for Compact Embedding RepresentationsTing Chen, Martin Renqiang Min, Yizhou Sun. 853-862 [doi]
• PixelSNAIL: An Improved Autoregressive Generative ModelXi Chen 0022, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel. 863-871 [doi]
• Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural NetworksMinmin Chen, Jeffrey Pennington, Samuel S. Schoenholz. 872-881 [doi]
• Learning to Explain: An Information-Theoretic Perspective on Model InterpretationJianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan. 882-891 [doi]
• Variational Inference and Model Selection with Generalized Evidence BoundsLiqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin. 892-901 [doi]
• DRACO: Byzantine-resilient Distributed Training via Redundant GradientsLingjiao Chen, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos. 902-911 [doi]
• SADAGRAD: Strongly Adaptive Stochastic Gradient MethodsZaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang. 912-920 [doi]
• Covariate Adjusted Precision Matrix Estimation via Nonconvex OptimizationJinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma 0004, Quanquan Gu. 921-930 [doi]
• End-to-End Learning for the Deep Multivariate Probit ModelDi Chen, Yexiang Xue, Carla P. Gomes. 931-940 [doi]
• Stochastic Training of Graph Convolutional Networks with Variance ReductionJianfei Chen, Jun Zhu 0001, Le Song. 941-949 [doi]
• Extreme Learning to Rank via Low Rank AssumptionMinhao Cheng, Ian Davidson, Cho-Jui Hsieh. 950-959 [doi]
• Learning a Mixture of Two Multinomial LogitsFlavio Chierichetti, Ravi Kumar 0001, Andrew Tomkins. 960-968 [doi]
• Structured Evolution with Compact Architectures for Scalable Policy OptimizationKrzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller. 969-977 [doi]
• Path Consistency Learning in Tsallis Entropy Regularized MDPsYinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh. 978-987 [doi]
• An Iterative, Sketching-based Framework for Ridge RegressionAgniva Chowdhury, Jiasen Yang, Petros Drineas. 988-997 [doi]
• Stochastic Wasserstein BarycentersSebastian Claici, Edward Chien, Justin Solomon. 998-1007 [doi]
• Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory EmbeddingsJohn D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine. 1008-1017 [doi]
• On Acceleration with Noise-Corrupted GradientsMichael Cohen 0001, Jelena Diakonikolas, Lorenzo Orecchia. 1018-1027 [doi]
• Online Linear Quadratic ControlAlon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar. 1028-1037 [doi]
• GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer. 1038-1047 [doi]
• Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-NormsGraham Cormode, Charlie Dickens, David P. Woodruff. 1048-1056 [doi]
• Efficient ModelBased Deep Reinforcement Learning with Variational State TabulationDane S. Corneil, Wulfram Gerstner, Johanni Brea. 1057-1066 [doi]
• Online Learning with AbstentionCorinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang. 1067-1075 [doi]
• Constrained Interacting Submodular GroupingsAndrew Cotter, Mahdi Milani Fard, Seungil You, Maya R. Gupta, Jeff A. Bilmes. 1076-1085 [doi]
• Inference Suboptimality in Variational AutoencodersChris Cremer, Xuechen Li, David K. Duvenaud. 1086-1094 [doi]
• Mix & Match Agent Curricula for Reinforcement LearningWojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu. 1095-1103 [doi]
• Implicit Quantile Networks for Distributional Reinforcement LearningWill Dabney, Georg Ostrovski, David Silver, Rémi Munos. 1104-1113 [doi]
• Learning Steady-States of Iterative Algorithms over GraphsHanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song. 1114-1122 [doi]
• Adversarial Attack on Graph Structured DataHanjun Dai, Hui Li, Tian Tian 0001, Xin Huang, Lin Wang, Jun Zhu 0001, Le Song. 1123-1132 [doi]
• SBEED: Convergent Reinforcement Learning with Nonlinear Function ApproximationBo Dai, Albert Shaw, Lihong Li 0001, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song. 1133-1142 [doi]
• Compressing Neural Networks using the Variational Information BottleneckBin Dai, Chen Zhu, Baining Guo, David P. Wipf. 1143-1152 [doi]
• Asynchronous Byzantine Machine Learning (the case of SGD)Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki. 1153-1162 [doi]
• Escaping Saddles with Stochastic GradientsHadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann. 1163-1172 [doi]
• Minibatch Gibbs Sampling on Large Graphical ModelsChristopher De Sa, Vincent Chen, Wing Wong. 1173-1181 [doi]
• Stochastic Video Generation with a Learned PriorEmily Denton, Rob Fergus. 1182-1191 [doi]
• Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive LearningStefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft. 1192-1201 [doi]
• Accurate Inference for Adaptive Linear ModelsYash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy. 1202-1211 [doi]
• Variational Network Inference: Strong and Stable with Concrete SupportAmir Dezfouli, Edwin V. Bonilla, Richard Nock. 1212-1221 [doi]
• Modeling Sparse Deviations for Compressed Sensing using Generative ModelsManik Dhar, Aditya Grover, Stefano Ermon. 1222-1231 [doi]
• Alternating Randomized Block Coordinate DescentJelena Diakonikolas, Lorenzo Orecchia. 1232-1240 [doi]
• Learning to Act in Decentralized Partially Observable MDPsJilles Steeve Dibangoye, Olivier Buffet. 1241-1250 [doi]
• Noisin: Unbiased Regularization for Recurrent Neural NetworksAdji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei. 1251-1260 [doi]
• Discovering and Removing Exogenous State Variables and Rewards for Reinforcement LearningThomas G. Dietterich, George Trimponias, Zhitang Chen. 1261-1269 [doi]
• Coordinated Exploration in Concurrent Reinforcement LearningMaria Dimakopoulou, Benjamin Van Roy. 1270-1278 [doi]
• Probabilistic Recurrent State-Space ModelsAndreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe. 1279-1288 [doi]
• Randomized Block Cubic Newton MethodNikita Doikov, Peter Richtárik. 1289-1297 [doi]
• Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph ClusteringAhmed Douik, Babak Hassibi. 1298-1307 [doi]
• Essentially No Barriers in Neural Network Energy LandscapeFelix Dräxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht. 1308-1317 [doi]
• Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic BuyerAlexey Drutsa. 1318-1327 [doi]
• On the Power of Over-parametrization in Neural Networks with Quadratic ActivationSimon S. Du, Jason D. Lee. 1328-1337 [doi]
• Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local MinimaSimon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos. 1338-1347 [doi]
• Investigating Human Priors for Playing Video GamesRachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei A. Efros. 1348-1356 [doi]
• A Distributed Second-Order Algorithm You Can TrustCelestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi. 1357-1365 [doi]
• Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's AlgorithmPavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin. 1366-1375 [doi]
• Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priorsGintare Karolina Dziugaite, Daniel M. Roy 0001. 1376-1385 [doi]
• Beyond the One-Step Greedy Approach in Reinforcement LearningYonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor. 1386-1395 [doi]
• Parallel and Streaming Algorithms for K-Core DecompositionHossein Esfandiari, Silvio Lattanzi, Vahab S. Mirrokni. 1396-1405 [doi]
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner ArchitecturesLasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu. 1406-1415 [doi]
• Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)Trefor W. Evans, Prasanth B. Nair. 1416-1425 [doi]
• The Limits of Maxing, Ranking, and Preference LearningMoein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar. 1426-1435 [doi]
• BOHB: Robust and Efficient Hyperparameter Optimization at ScaleStefan Falkner, Aaron Klein, Frank Hutter. 1436-1445 [doi]
• More Robust Doubly Robust Off-policy EvaluationMehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh. 1446-1455 [doi]
• Efficient and Consistent Adversarial Bipartite MatchingRizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart. 1456-1465 [doi]
• Global Convergence of Policy Gradient Methods for the Linear Quadratic RegulatorMaryam Fazel, Rong Ge 0001, Sham Kakade, Mehran Mesbahi. 1466-1475 [doi]
• CRVI: Convex Relaxation for Variational InferenceGhazal Fazelnia, John Paisley. 1476-1484 [doi]
• Fourier Policy GradientsMatthew Fellows, Kamil Ciosek, Shimon Whiteson. 1485-1494 [doi]
• Nonparametric variable importance using an augmented neural network with multi-task learningJean Feng, Brian D. Williamson, Marco Carone, Noah Simon. 1495-1504 [doi]
• Closed-form Marginal Likelihood in Gamma-Poisson Matrix FactorizationLouis Filstroff, Alberto Lumbreras, Cédric Févotte. 1505-1513 [doi]
• Automatic Goal Generation for Reinforcement Learning AgentsCarlos Florensa, David Held, Xinyang Geng, Pieter Abbeel. 1514-1523 [doi]
• DiCE: The Infinitely Differentiable Monte Carlo EstimatorJakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson. 1524-1533 [doi]
• Practical Contextual Bandits with Regression OraclesDylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire. 1534-1543 [doi]
• Generative Temporal Models with Spatial Memory for Partially Observed EnvironmentsMarco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola. 1544-1553 [doi]
• ADMM and Accelerated ADMM as Continuous Dynamical SystemsGuilherme França, Daniel P. Robinson, René Vidal. 1554-1562 [doi]
• Bilevel Programming for Hyperparameter Optimization and Meta-LearningLuca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil. 1563-1572 [doi]
• Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningRonan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner. 1573-1581 [doi]
• Addressing Function Approximation Error in Actor-Critic MethodsScott Fujimoto, Herke van Hoof, David Meger. 1582-1591 [doi]
• Clipped Action Policy GradientYasuhiro Fujita 0001, Shin-ichi Maeda. 1592-1601 [doi]
• Born-Again Neural NetworksTommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar. 1602-1611 [doi]
• Local Private Hypothesis Testing: Chi-Square TestsMarco Gaboardi, Ryan Rogers 0002. 1612-1621 [doi]
• Inductive Two-layer Modeling with Parametric Bregman TransferVignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu. 1622-1631 [doi]
• Hyperbolic Entailment Cones for Learning Hierarchical EmbeddingsOctavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann. 1632-1641 [doi]
• Parameterized Algorithms for the Matrix Completion ProblemRobert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider. 1642-1651 [doi]
• Synthesizing Programs for Images using Reinforced Adversarial LearningYaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals. 1652-1661 [doi]
• Spotlight: Optimizing Device Placement for Training Deep Neural NetworksYuanxiang Gao, Li Chen 0019, Baochun Li. 1662-1670 [doi]
• Parallel Bayesian Network Structure LearningTian Gao, Dennis Wei. 1671-1680 [doi]
• Structured Output Learning with Abstention: Application to Accurate Opinion PredictionAlexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc. 1681-1689 [doi]
• Conditional Neural ProcessesMarta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami. 1690-1699 [doi]
• Temporal Poisson Square Root Graphical ModelsSinong Geng, Zhaobin Kuang, Peggy L. Peissig, David Page. 1700-1709 [doi]
• The Generalization Error of Dictionary Learning with Moreau EnvelopesAlexandros Georgogiannis. 1710-1718 [doi]
• Budgeted Experiment Design for Causal Structure LearningAmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim. 1719-1728 [doi]
• Linear Spectral Estimators and an Application to Phase RetrievalRamina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer. 1729-1738 [doi]
• Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsSoumya Ghosh, Jiayu Yao, Finale Doshi-Velez. 1739-1748 [doi]
• Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial TimeAsish Ghoshal, Jean Honorio. 1749-1757 [doi]
• Robust and Scalable Models of Microbiome DynamicsTravis E. Gibson, Georg K. Gerber. 1758-1767 [doi]
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• Learning One Convolutional Layer with Overlapping PatchesSurbhi Goel, Adam R. Klivans, Raghu Meka. 1778-1786 [doi]
• Visualizing and Understanding Atari AgentsSamuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern. 1787-1796 [doi]
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• Characterizing Implicit Bias in Terms of Optimization GeometrySuriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro. 1827-1836 [doi]
• Shampoo: Preconditioned Stochastic Tensor OptimizationVineet Gupta 0001, Tomer Koren, Yoram Singer. 1837-1845 [doi]
• Latent Space Policies for Hierarchical Reinforcement LearningTuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine. 1846-1855 [doi]
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic ActorTuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine. 1856-1865 [doi]
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• K-Beam Minimax: Efficient Optimization for Deep Adversarial LearningJihun Hamm, Yung-Kyun Noh. 1876-1884 [doi]
• Candidates vs. Noises Estimation for Large Multi-Class Classification ProblemLei Han, Yiheng Huang, Tong Zhang. 1885-1894 [doi]
• Stein Variational Gradient Descent Without GradientJun Han 0002, Qiang Liu 0001. 1895-1903 [doi]
• Rectify Heterogeneous Models with Semantic MappingHan-Jia Ye, De-Chuan Zhan, Yuan Jiang 0001, Zhi-Hua Zhou. 1904-1913 [doi]
• Deep Models of Interactions Across SetsJason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh. 1914-1923 [doi]
• Learning Memory Access PatternsMilad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan. 1924-1933 [doi]
• Fairness Without Demographics in Repeated Loss MinimizationTatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang. 1934-1943 [doi]
• Multicalibration: Calibration for the (Computationally-Identifiable) MassesÚrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum. 1944-1953 [doi]
• Recurrent Predictive State Policy NetworksAhmed Hefny, Zita Marinho, Wen Sun 0002, Siddhartha Srinivasa, Geoffrey J. Gordon. 1954-1963 [doi]
• Learning unknown ODE models with Gaussian processesMarkus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki. 1964-1973 [doi]
• Orthogonal Recurrent Neural Networks with Scaled Cayley TransformKyle Helfrich, Devin Willmott, Qiang Ye. 1974-1983 [doi]
• Fast Bellman Updates for Robust MDPsChin Pang Ho, Marek Petrik, Wolfram Wiesemann. 1984-1993 [doi]
• CyCADA: Cycle-Consistent Adversarial Domain AdaptationJudy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell. 1994-2003 [doi]
• Sound Abstraction and Decomposition of Probabilistic ProgramsSteven Holtzen, Guy Van den Broeck, Todd D. Millstein. 2004-2013 [doi]
• Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over NetworksMingyi Hong, Meisam Razaviyayn, Jason Lee. 2014-2023 [doi]
• Variational Bayesian dropout: pitfalls and fixesJiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani. 2024-2033 [doi]
• Does Distributionally Robust Supervised Learning Give Robust Classifiers?Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama. 2034-2042 [doi]
• Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite ProgramsBin Hu, Stephen Wright, Laurent Lessard. 2043-2052 [doi]
• Near Optimal Frequent Directions for Sketching Dense and Sparse MatricesZengfeng Huang. 2053-2062 [doi]
• Learning Deep ResNet Blocks Sequentially using Boosting TheoryFurong Huang, Jordan T. Ash, John Langford 0001, Robert E. Schapire. 2063-2072 [doi]
• Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic ModelingKejun Huang, Xiao Fu 0001, Nicholas D. Sidiropoulos. 2073-2082 [doi]
• Neural Autoregressive FlowsChin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville. 2083-2092 [doi]
• Topological Mixture EstimationSteve Huntsman. 2093-2102 [doi]
• Decoupled Parallel Backpropagation with Convergence GuaranteeZhouyuan Huo, Bin Gu, Qian Yang, Heng Huang. 2103-2111 [doi]
• Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningRodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, Sheila A. McIlraith. 2112-2121 [doi]
• Deep Variational Reinforcement Learning for POMDPsMaximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson. 2122-2131 [doi]
• Attention-based Deep Multiple Instance LearningMaximilian Ilse, Jakub M. Tomczak, Max Welling. 2132-2141 [doi]
• Black-box Adversarial Attacks with Limited Queries and InformationAndrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin. 2142-2151 [doi]
• Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering ModelHideaki Imamura, Issei Sato, Masashi Sugiyama. 2152-2161 [doi]
• Improving Regression Performance with Distributional LossesEhsan Imani, Martha White. 2162-2171 [doi]
• Deep Density DestructorsDavid I. Inouye, Pradeep Ravikumar. 2172-2180 [doi]
• Unbiased Objective Estimation in Predictive OptimizationShinji Ito, Akihiro Yabe, Ryohei Fujimaki. 2181-2190 [doi]
• Anonymous Walk EmbeddingsSergey Ivanov, Evgeny Burnaev. 2191-2200 [doi]
• Learning Binary Latent Variable Models: A Tensor Eigenpair ApproachAriel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler. 2201-2210 [doi]
• Firing Bandits: Optimizing CrowdfundingLalit Jain, Kevin G. Jamieson. 2211-2219 [doi]
• Differentially Private Matrix Completion RevisitedPrateek Jain 0002, Om Dipakbhai Thakkar, Abhradeep Thakurta. 2220-2229 [doi]
• Video Prediction with Appearance and Motion ConditionsYunseok Jang, Gunhee Kim, Yale Song. 2230-2239 [doi]
• Pathwise Derivatives Beyond the Reparameterization TrickMartin Jankowiak, Fritz Obermeyer. 2240-2249 [doi]
• Detecting non-causal artifacts in multivariate linear regression modelsDominik Janzing, Bernhard Schölkopf. 2250-2258 [doi]
• A Unified Framework for Structured Low-rank Matrix LearningPratik Jawanpuria, Bamdev Mishra. 2259-2268 [doi]
• Efficient end-to-end learning for quantizable representationsYeonwoo Jeong, Hyun Oh Song. 2269-2278 [doi]
• Exploring Hidden Dimensions in Parallelizing Convolutional Neural NetworksZhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken. 2279-2288 [doi]
• Feedback-Based Tree Search for Reinforcement LearningDaniel R. Jiang, Emmanuel Ekwedike, Han Liu. 2289-2298 [doi]
• Quickshift++: Provably Good Initializations for Sample-Based Mean ShiftHeinrich Jiang, Jennifer Jang, Samory Kpotufe. 2299-2308 [doi]
• MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted LabelsLu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei. 2309-2318 [doi]
• The Weighted Kendall and High-order Kernels for PermutationsYunlong Jiao, Jean-Philippe Vert. 2319-2327 [doi]
• Junction Tree Variational Autoencoder for Molecular Graph GenerationWengong Jin, Regina Barzilay, Tommi S. Jaakkola. 2328-2337 [doi]
• Network Global Testing by Counting GraphletsJiashun Jin, Zheng Tracy Ke, Shengming Luo. 2338-2346 [doi]
• Regret Minimization for Partially Observable Deep Reinforcement LearningPeter H. Jin, Kurt Keutzer, Sergey Levine. 2347-2356 [doi]
• WSNet: Compact and Efficient Networks Through Weight SamplingXiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan. 2357-2366 [doi]
• Large-Scale Cox Process Inference using Variational Fourier FeaturesS. T. John, James Hensman. 2367-2375 [doi]
• Composite Functional Gradient Learning of Generative Adversarial ModelsRie Johnson, Tong Zhang 0001. 2376-2384 [doi]
• Kronecker Recurrent UnitsCijo Jose, Moustapha Cissé, François Fleuret. 2385-2394 [doi]
• Fast Decoding in Sequence Models Using Discrete Latent VariablesLukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer. 2395-2404 [doi]
• Kernel Recursive ABC: Point Estimation with Intractable LikelihoodTakafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu. 2405-2414 [doi]
• Efficient Neural Audio SynthesisNal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron Van Den Oord, Sander Dieleman, Koray Kavukcuoglu. 2415-2424 [doi]
• Learning Diffusion using HyperparametersDimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg. 2425-2433 [doi]
• Signal and Noise Statistics Oblivious Orthogonal Matching PursuitSreejith Kallummil, Sheetal Kalyani. 2434-2443 [doi]
• Residual Unfairness in Fair Machine Learning from Prejudiced DataNathan Kallus, Angela Zhou. 2444-2453 [doi]
• Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse AnnotationsAshwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra. 2454-2463 [doi]
• Semi-Supervised Learning via Compact Latent Space ClusteringKonstantinos Kamnitsas, Daniel C. Castro, Loïc Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya V. Nori. 2464-2473 [doi]
• Policy Optimization with DemonstrationsBingyi Kang, Zequn Jie, Jiashi Feng. 2474-2483 [doi]
• Improving Sign Random Projections With Additional InformationKeegan Kang, Wong Wei Pin. 2484-2492 [doi]
• Let's be Honest: An Optimal No-Regret Framework for Zero-Sum GamesEhsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher. 2493-2501 [doi]
• Continual Reinforcement Learning with Complex SynapsesChristos Kaplanis, Murray Shanahan, Claudia Clopath. 2502-2511 [doi]
• LaVAN: Localized and Visible Adversarial NoiseDanny Karmon, Daniel Zoran, Yoav Goldberg. 2512-2520 [doi]
• Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence AnalysisHiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra. 2521-2529 [doi]
• Not All Samples Are Created Equal: Deep Learning with Importance SamplingAngelos Katharopoulos, François Fleuret. 2530-2539 [doi]
• Feasible Arm IdentificationJulian Katz-Samuels, Clayton Scott. 2540-2548 [doi]
• Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness ConstraintsEhsan Kazemi 0001, Morteza Zadimoghaddam, Amin Karbasi. 2549-2558 [doi]
• Focused Hierarchical RNNs for Conditional Sequence ProcessingNan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher J. Pal. 2559-2568 [doi]
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup FairnessMichael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu. 2569-2577 [doi]
• Improved nearest neighbor search using auxiliary information and priority functionsOmid Keivani, Kaushik Sinha. 2578-2586 [doi]
• ContextNet: Deep learning for Star Galaxy ClassificationNoble Kennamer, David Kirkby, Alexander T. Ihler, Francisco Javier Sanchez-Lopez. 2587-2595 [doi]
• Frank-Wolfe with Subsampling OracleThomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont. 2596-2605 [doi]
• Convergence guarantees for a class of non-convex and non-smooth optimization problemsKoulik Khamaru, Martin J. Wainwright. 2606-2615 [doi]
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in AdamMohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava. 2616-2625 [doi]
• Geometry Score: A Method For Comparing Generative Adversarial NetworksValentin Khrulkov, Ivan V. Oseledets. 2626-2634 [doi]
• Blind Justice: Fairness with Encrypted Sensitive AttributesNiki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller. 2635-2644 [doi]
• Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events DataMinyoung Kim. 2645-2653 [doi]
• Disentangling by FactorisingHyunjik Kim, Andriy Mnih. 2654-2663 [doi]
• Self-Bounded Prediction Suffix Tree via Approximate String MatchingDongwoo Kim, Christian J. Walder. 2664-2672 [doi]
• Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda B. Viégas, Rory Sayres. 2673-2682 [doi]
• Semi-Amortized Variational AutoencodersYoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush. 2683-2692 [doi]
• Neural Relational Inference for Interacting SystemsThomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel. 2693-2702 [doi]
• An Alternative View: When Does SGD Escape Local Minima?Robert Kleinberg, Yuanzhi Li, Yang Yuan. 2703-2712 [doi]
• Crowdsourcing with Arbitrary AdversariesMatthäus Kleindessner, Pranjal Awasthi. 2713-2722 [doi]
• Spatio-temporal Bayesian On-line Changepoint Detection with Model SelectionJeremias Knoblauch, Theodoros Damoulas. 2723-2732 [doi]
• Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control FrameworkArman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky. 2733-2741 [doi]
• Nonconvex Optimization for Regression with Fairness ConstraintsJunpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao. 2742-2751 [doi]
• On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact GroupsRisi Kondor, Shubhendu Trivedi. 2752-2760 [doi]
• Compiling Combinatorial Prediction GamesFrédéric Koriche. 2761-2770 [doi]
• Dynamic Evaluation of Neural Sequence ModelsBen Krause, Emmanuel Kahembwe, Iain Murray 0001, Steve Renals. 2771-2780 [doi]
• Semiparametric Contextual BanditsAkshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis. 2781-2790 [doi]
• Fast Maximization of Non-Submodular, Monotonic Functions on the Integer LatticeAlan Kuhnle, J. David Smith, Victoria G. Crawford, My T. Thai. 2791-2800 [doi]
• Accurate Uncertainties for Deep Learning Using Calibrated RegressionVolodymyr Kuleshov, Nathan Fenner, Stefano Ermon. 2801-2809 [doi]
• Trainable Calibration Measures For Neural Networks From Kernel Mean EmbeddingsAviral Kumar, Sunita Sarawagi, Ujjwal Jain. 2810-2819 [doi]
• Data-Dependent Stability of Stochastic Gradient DescentIlja Kuzborskij, Christoph H. Lampert. 2820-2829 [doi]
• Explicit Inductive Bias for Transfer Learning with Convolutional NetworksXuhong Li, Yves Grandvalet, Franck Davoine. 2830-2839 [doi]
• Understanding the Loss Surface of Neural Networks for Binary ClassificationShiyu Liang, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant. 2840-2849 [doi]
• Mixed batches and symmetric discriminators for GAN trainingThomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek. 2850-2859 [doi]
• Binary Partitions with Approximate Minimum ImpurityEduardo Sany Laber, Marco Molinaro, Felipe de A. Mello Pereira. 2860-2868 [doi]
• Canonical Tensor Decomposition for Knowledge Base CompletionTimothée Lacroix, Nicolas Usunier, Guillaume Obozinski. 2869-2878 [doi]
• Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent NetworksBrenden M. Lake, Marco Baroni. 2879-2888 [doi]
• An Estimation and Analysis Framework for the Rasch ModelAndrew S. Lan, Mung Chiang, Christoph Studer. 2889-2897 [doi]
• Partial Optimality and Fast Lower Bounds for Weighted Correlation ClusteringJan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres. 2898-2907 [doi]
• Deep Linear Networks with Arbitrary Loss: All Local Minima Are GlobalThomas Laurent, James von Brecht. 2908-2913 [doi]
• The Multilinear Structure of ReLU NetworksThomas Laurent, James von Brecht. 2914-2922 [doi]
• Hierarchical Imitation and Reinforcement LearningHoang Minh Le 0001, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III. 2923-2932 [doi]
• Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceYoonho Lee, Seungjin Choi. 2933-2942 [doi]
• Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated CurlingKyowoon Lee, Sol-A. Kim, Jaesik Choi, Seong-Whan Lee. 2943-2952 [doi]
• Gated Path Planning NetworksLisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov. 2953-2961 [doi]
• Deep Asymmetric Multi-task Feature LearningHaebeom Lee, Eunho Yang, Sung Ju Hwang. 2962-2970 [doi]
• Noise2Noise: Learning Image Restoration without Clean DataJaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila. 2971-2980 [doi]
• Out-of-sample extension of graph adjacency spectral embeddingKeith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe. 2981-2990 [doi]
• An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural NetworksQianxiao Li, Shuji Hao. 2991-3000 [doi]
• Towards Binary-Valued Gates for Robust LSTM TrainingZhuohan Li, Di He, Fei Tian, Wei Chen 0034, Tao Qin, Liwei Wang 0001, Tie-Yan Liu. 3001-3010 [doi]
• On the Limitations of First-Order Approximation in GAN DynamicsJerry Li 0001, Aleksander Madry, John Peebles, Ludwig Schmidt. 3011-3019 [doi]
• Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral ClusteringPan Li, Olgica Milenkovic. 3020-3029 [doi]
• The Well-Tempered LassoYuanzhi Li, Yoram Singer. 3030-3038 [doi]
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• Asynchronous Decentralized Parallel Stochastic Gradient DescentXiangru Lian, Wei Zhang, Ce Zhang, Ji Liu. 3049-3058 [doi]
• RLlib: Abstractions for Distributed Reinforcement LearningEric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica. 3059-3068 [doi]
• On the Spectrum of Random Features Maps of High Dimensional DataZhenyu Liao, Romain Couillet. 3069-3077 [doi]
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• Iterative Amortized InferenceJoseph Marino, Yisong Yue, Stephan Mandt. 3400-3409 [doi]
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• 1 RegularizationPoorya Mianjy, Raman Arora. 3528-3536 [doi]
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• Differentiable Abstract Interpretation for Provably Robust Neural NetworksMatthew Mirman, Timon Gehr, Martin T. Vechev. 3575-3583 [doi]
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• Data Summarization at Scale: A Two-Stage Submodular ApproachMarko Mitrovic, Ehsan Kazemi 0001, Morteza Zadimoghaddam, Amin Karbasi. 3593-3602 [doi]
• The Hierarchical Adaptive Forgetting Variational FilterVincent Moens. 3603-3612 [doi]
• Decentralized Submodular Maximization: Bridging Discrete and Continuous SettingsAryan Mokhtari, Hamed Hassani, Amin Karbasi. 3613-3622 [doi]
• DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse CodingThomas Moreau, Laurent Oudre, Nicolas Vayatis. 3623-3631 [doi]
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• Dropout Training, Data-dependent Regularization, and Generalization BoundsWenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang 0001. 3642-3650 [doi]
• Kernelized Synaptic Weight MatricesLorenz K. Müller, Julien N. P. Martel, Giacomo Indiveri. 3651-3660 [doi]
• Rapid Adaptation with Conditionally Shifted NeuronsTsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler. 3661-3670 [doi]
• On the Relationship between Data Efficiency and Error for Uncertainty SamplingStephen Mussmann, Percy Liang. 3671-3679 [doi]
• Fitting New Speakers Based on a Short Untranscribed SampleEliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf. 3680-3688 [doi]
• Smoothed Action Value Functions for Learning Gaussian PoliciesOfir Nachum, Mohammad Norouzi 0002, George Tucker, Dale Schuurmans. 3689-3697 [doi]
• Nearly Optimal Robust Subspace TrackingPraneeth Narayanamurthy, Namrata Vaswani. 3698-3706 [doi]
• Stochastic Proximal Algorithms for AUC MaximizationMichael Natole, Yiming Ying, Siwei Lyu. 3707-3716 [doi]
• Mitigating Bias in Adaptive Data Gathering via Differential PrivacySeth Neel, Aaron Roth. 3717-3726 [doi]
• Optimization Landscape and Expressivity of Deep CNNsQuynh Nguyen, Matthias Hein 0001. 3727-3736 [doi]
• Neural Networks Should Be Wide Enough to Learn Disconnected Decision RegionsQuynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein 0001. 3737-3746 [doi]
• SGD and Hogwild! Convergence Without the Bounded Gradients AssumptionLam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác. 3747-3755 [doi]
• Active Testing: An Efficient and Robust Framework for Estimating AccuracyPhuc Xuan Nguyen, Deva Ramanan, Charless C. Fowlkes. 3756-3765 [doi]
• On Learning Sparsely Used Dictionaries from Incomplete SamplesThanh Nguyen, Akshay Soni, Chinmay Hegde. 3766-3775 [doi]
• Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic GeometryMaximilian Nickel, Douwe Kiela. 3776-3785 [doi]
• State Space Gaussian Processes with Non-Gaussian LikelihoodHannes Nickisch, Arno Solin, Alexander Grigorevskiy. 3786-3795 [doi]
• SparseMAP: Differentiable Sparse Structured InferenceVlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie. 3796-3805 [doi]
• A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based VisualizationsWeili Nie, Yang Zhang, Ankit Patel. 3806-3815 [doi]
• Functional Gradient Boosting based on Residual Network PerceptionAtsushi Nitanda, Taiji Suzuki. 3816-3825 [doi]
• Beyond 1/2-Approximation for Submodular Maximization on Massive Data StreamsAshkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson. 3826-3835 [doi]
• The Uncertainty Bellman Equation and ExplorationBrendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih. 3836-3845 [doi]
• Is Generator Conditioning Causally Related to GAN Performance?Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow. 3846-3855 [doi]
• Learning in Reproducing Kernel Krein SpacesDino Oglic, Thomas Gärtner. 3856-3864 [doi]
• BOCK : Bayesian Optimization with Cylindrical KernelsChangYong Oh, Efstratios Gavves, Max Welling. 3865-3874 [doi]
• Self-Imitation LearningJunhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee. 3875-3884 [doi]
• A probabilistic framework for multi-view feature learning with many-to-many associations via neural networksAkifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira. 3885-3894 [doi]
• Transformation Autoregressive NetworksJunier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider. 3895-3904 [doi]
• Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven ApproachesSimon Olofsson, Marc Peter Deisenroth, Ruth Misener. 3905-3914 [doi]
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• Learning Localized Spatio-Temporal Models From Streaming DataMuhammad Osama, Dave Zachariah, Thomas B. Schön. 3924-3932 [doi]
• Autoregressive Quantile Networks for Generative ModelingGeorg Ostrovski, Will Dabney, Rémi Munos. 3933-3942 [doi]
• Efficient First-Order Algorithms for Adaptive Signal DenoisingDmitrii Ostrovskii, Zaïd Harchaoui. 3943-3952 [doi]
• Analyzing Uncertainty in Neural Machine TranslationMyle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato. 3953-3962 [doi]
• Learning Compact Neural Networks with RegularizationSamet Oymak. 3963-3972 [doi]
• Tree Edit Distance Learning via Adaptive Symbol EmbeddingsBenjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer. 3973-3982 [doi]
• Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation ControlYangchen Pan, Amir Massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski. 3983-3992 [doi]
• Learning to Speed Up Structured Output PredictionXingyuan Pan, Vivek Srikumar. 3993-4002 [doi]
• Theoretical Analysis of Image-to-Image Translation with Adversarial LearningXudong Pan, Mi Zhang, Daizong Ding. 4003-4012 [doi]
• Max-Mahalanobis Linear Discriminant Analysis NetworksTianyu Pang, Chao Du, Jun Zhu 0001. 4013-4022 [doi]
• Stochastic Variance-Reduced Policy GradientMatteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli. 4023-4032 [doi]
• Learning Independent Causal MechanismsGiambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf. 4033-4041 [doi]
• Time Limits in Reinforcement LearningFabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev. 4042-4051 [doi]
• Image TransformerNiki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran. 4052-4061 [doi]
• PIPPS: Flexible Model-Based Policy Search Robust to the Curse of ChaosPaavo Parmas, Carl Edward Rasmussen, Jan Peters 0001, Kenji Doya. 4062-4071 [doi]
• High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled ApproachTim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely. 4072-4081 [doi]
• Adaptive Three Operator SplittingFabian Pedregosa, Gauthier Gidel. 4082-4091 [doi]
• Efficient Neural Architecture Search via Parameter SharingHieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. 4092-4101 [doi]
• Bandits with Delayed, Aggregated Anonymous FeedbackCiara Pike-Burke, Shipra Agrawal 0001, Csaba Szepesvári, Steffen Grünewälder. 4102-4110 [doi]
• Constant-Time Predictive Distributions for Gaussian ProcessesGeoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson. 4111-4120 [doi]
• Local Convergence Properties of SAGA/Prox-SVRG and AccelerationClarice Poon, Jingwei Liang, Carola-Bibiane Schoenlieb. 4121-4129 [doi]
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• Learning Dynamics of Linear Denoising AutoencodersArnu Pretorius, Steve Kroon, Herman Kamper. 4138-4147 [doi]
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• Selecting Representative Examples for Program SynthesisYewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Pack Kaelbling. 4158-4167 [doi]
• Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future PredictionSiyuan Qi, Baoxiong Jia, Song Chun Zhu. 4168-4176 [doi]
• Do Outliers Ruin Collaboration?Mingda Qiao. 4177-4184 [doi]
• Gradually Updated Neural Networks for Large-Scale Image RecognitionSiyuan Qiao, Zhishuai Zhang, Wei Shen 0002, Bo Wang, Alan L. Yuille. 4185-4194 [doi]
• DCFNet: Deep Neural Network with Decomposed Convolutional FiltersQiang Qiu, Xiuyuan Cheng, A. Robert Calderbank, Guillermo Sapiro. 4195-4204 [doi]
• Non-convex Conditional Gradient SlidingChao Qu, Yan Li, Huan Xu. 4205-4214 [doi]
• Machine Theory of MindNeil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick. 4215-4224 [doi]
• Fast Parametric Learning with Activation MemorizationJack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap. 4225-4234 [doi]
• Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg. 4235-4243 [doi]
• Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total VariationHugo Raguet, Loïc Landrieu. 4244-4253 [doi]
• Modeling Others using Oneself in Multi-Agent Reinforcement LearningRoberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus. 4254-4263 [doi]
• On Nesting Monte Carlo EstimatorsTom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington. 4264-4273 [doi]
• Tighter Variational Bounds are Not Necessarily BetterTom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh. 4274-4282 [doi]
• SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery RateAaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan. 4283-4291 [doi]
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• Gradient Coding from Cyclic MDS Codes and Expander GraphsNetanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo. 4302-4310 [doi]
• Learning Implicit Generative Models with the Method of Learned MomentsSuman V. Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals. 4311-4320 [doi]
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• Fast Information-theoretic Bayesian OptimisationBin Xin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne. 4381-4389 [doi]
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• Augment and Reduce: Stochastic Inference for Large Categorical DistributionsFrancisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei. 4400-4409 [doi]
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• Locally Private Hypothesis TestingOr Sheffet. 4612-4621 [doi]
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• Scalable Approximate Bayesian Inference for Particle Tracking DataRuoxi Sun, Liam Paninski. 4807-4816 [doi]
• Graphical Nonconvex Optimization via an Adaptive Convex RelaxationQiang Sun, Kean Ming Tan, Han Liu 0001, Tong Zhang 0001. 4817-4824 [doi]
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• Differentiable Compositional Kernel Learning for Gaussian ProcessesShengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse. 4835-4844 [doi]
• Learning the Reward Function for a Misspecified ModelErik Talvitie. 4845-4854 [doi]
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• Decoupling Gradient-Like Learning Rules from RepresentationsPhilip S. Thomas, Christoph Dann, Emma Brunskill. 4924-4932 [doi]
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• Stein Variational Message Passing for Continuous Graphical ModelsDilin Wang, Zhe Zeng, Qiang Liu 0001. 5206-5214 [doi]
• Approximate Leave-One-Out for Fast Parameter Tuning in High DimensionsShuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni. 5215-5224 [doi]
• Hierarchical Multi-Label Classification NetworksJonatas Wehrmann, Ricardo Cerri, Rodrigo C. Barros. 5225-5234 [doi]
• Curriculum Learning by Transfer Learning: Theory and Experiments with Deep NetworksDaphna Weinshall, Gad Cohen, Dan Amir. 5235-5243 [doi]
• Extracting Automata from Recurrent Neural Networks Using Queries and CounterexamplesGail Weiss, Yoav Goldberg, Eran Yahav. 5244-5253 [doi]
• LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm ConfigurationGellért Weisz, Andrés György, Csaba Szepesvári. 5254-5262 [doi]
• Deep Predictive Coding Network for Object RecognitionHaiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu. 5263-5272 [doi]
• Towards Fast Computation of Certified Robustness for ReLU NetworksTsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon. 5273-5282 [doi]
• Provable Defenses against Adversarial Examples via the Convex Outer Adversarial PolytopeEric Wong, J. Zico Kolter. 5283-5292 [doi]
• Local Density Estimation in High DimensionsXian Wu, Moses Charikar, Vishnu Natchu. 5293-5301 [doi]
• Adaptive Exploration-Exploitation Tradeoff for Opportunistic BanditsHuasen Wu, Xueying Guo, Xin Liu. 5302-5310 [doi]
• SQL-Rank: A Listwise Approach to Collaborative RankingLiwei Wu, Cho-Jui Hsieh, James Sharpnack. 5311-5320 [doi]
• Error Compensated Quantized SGD and its Applications to Large-scale Distributed OptimizationJiaxiang Wu, Weidong Huang 0005, JunZhou Huang, Tong Zhang 0001. 5321-5329 [doi]
• Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial TrainingXi Wu 0001, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha. 5330-5338 [doi]
• Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference AlgorithmsYi Wu 0013, Siddharth Srivastava 0001, Nicholas Hay, Simon S. Du, Stuart Russell 0001. 5339-5348 [doi]
• Variance Regularized Counterfactual Risk Minimization via Variational Divergence MinimizationHang Wu, May D. Wang. 5349-5358 [doi]
• Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep ConvolutionsJunru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin. 5359-5368 [doi]
• Bayesian Quadrature for Multiple Related IntegralsXiaoyue Xi, François-Xavier Briol, Mark A. Girolami. 5369-5378 [doi]
• Model-Level Dual LearningYingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu. 5379-5388 [doi]
• Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural NetworksLechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington. 5389-5398 [doi]
• Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical AnalysisPengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing. 5399-5408 [doi]
• Nonoverlap-Promoting Variable SelectionPengtao Xie, Hongbao Zhang, Yichen Zhu, Eric P. Xing. 5409-5418 [doi]
• Learning Semantic Representations for Unsupervised Domain AdaptationShaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen. 5419-5428 [doi]
• Rates of Convergence of Spectral Methods for Graphon EstimationJiaming Xu. 5429-5438 [doi]
• Learning Registered Point Processes from Idiosyncratic ObservationsHongteng Xu, Lawrence Carin, Hongyuan Zha. 5439-5448 [doi]
• Representation Learning on Graphs with Jumping Knowledge NetworksKeyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. 5449-5458 [doi]
• Learning to Explore via Meta-Policy GradientTianbing Xu, Qiang Liu, Liang Zhao, Jian Peng. 5459-5468 [doi]
• Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal InformationYichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski. 5469-5478 [doi]
• Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive DataGanggang Xu, Zuofeng Shang, Guang Cheng. 5479-5487 [doi]
• Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex FunctionsPan Xu, Tianhao Wang 0002, Quanquan Gu. 5488-5497 [doi]
• A Semantic Loss Function for Deep Learning with Symbolic KnowledgeJingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van den Broeck. 5498-5507 [doi]
• Causal Bandits with Propagating InferenceAkihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi. 5508-5516 [doi]
• Active Learning with Logged DataSongbai Yan, Kamalika Chaudhuri, Tara Javidi. 5517-5526 [doi]
• Binary Classification with Karmic, Threshold-Quasi-Concave MetricsBowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar. 5527-5536 [doi]
• Characterizing and Learning Equivalence Classes of Causal DAGs under InterventionsKarren Yang, Abigail Katoff, Caroline Uhler. 5537-5546 [doi]
• Dependent Relational Gamma Process Models for Longitudinal NetworksSikun Yang, Heinz Koeppl. 5547-5556 [doi]
• Goodness-of-fit Testing for Discrete Distributions via Stein DiscrepancyJiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville. 5557-5566 [doi]
• Mean Field Multi-Agent Reinforcement LearningYaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang. 5567-5576 [doi]
• Yes, but Did It Work?: Evaluating Variational InferenceYuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman. 5577-5586 [doi]
• Hierarchical Text Generation and Planning for Strategic DialogueDenis Yarats, Mike Lewis. 5587-5595 [doi]
• p DistancesGrigory Yaroslavtsev, Adithya Vadapalli. 5596-5605 [doi]
• Communication-Computation Efficient Gradient CodingMin Ye, Emmanuel Abbe. 5606-5615 [doi]
• Variable Selection via Penalized Neural Network: a Drop-Out-One Loss ApproachMao Ye, Yan Sun. 5616-5625 [doi]
• Loss Decomposition for Fast Learning in Large Output SpacesIan En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar. 5626-5635 [doi]
• Byzantine-Robust Distributed Learning: Towards Optimal Statistical RatesDong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett. 5636-5645 [doi]
• Semi-Implicit Variational InferenceMingzhang Yin, Mingyuan Zhou. 5646-5655 [doi]
• Disentangled Sequential AutoencoderYingzhen Li, Stephan Mandt. 5656-5665 [doi]
• Probably Approximately Metric-Fair LearningGal Yona, Guy N. Rothblum. 5666-5674 [doi]
• GAIN: Missing Data Imputation using Generative Adversarial NetsJinsung Yoon, James Jordon, Mihaela van der Schaar. 5675-5684 [doi]
• RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial NetworksJinsung Yoon, James Jordon, Mihaela van der Schaar. 5685-5693 [doi]
• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive ModelsJiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec. 5694-5703 [doi]
• An Efficient Semismooth Newton Based Algorithm for Convex ClusteringYancheng Yuan, Defeng Sun, Kim-Chuan Toh. 5704-5712 [doi]
• A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite ProgrammingAlp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher. 5713-5722 [doi]
• Orthogonal Machine Learning: Power and LimitationsIlias Zadik, Lester W. Mackey, Vasilis Syrgkanis. 5723-5731 [doi]
• Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPsAndrea Zanette, Emma Brunskill. 5732-5740 [doi]
• Policy Optimization as Wasserstein Gradient FlowsRuiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin. 5741-5750 [doi]
• Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes FlowXiao Zhang, Simon S. Du, Quanquan Gu. 5751-5760 [doi]
• Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix CompletionRichard Y. Zhang, Salar Fattahi, Somayeh Sojoudi. 5761-5770 [doi]
• High Performance Zero-Memory Overhead Direct ConvolutionsJiyuan Zhang, Franz Franchetti, Tze Meng Low. 5771-5780 [doi]
• Safe Element Screening for Submodular Function MinimizationWeizhong Zhang, Bin Hong, Lin Ma 0002, Wei Liu 0005, Tong Zhang. 5781-5790 [doi]
• Improving the Privacy and Accuracy of ADMM-Based Distributed AlgorithmsXueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu. 5791-5800 [doi]
• Stabilizing Gradients for Deep Neural Networks via Efficient SVD ParameterizationJiong Zhang, Qi Lei, Inderjit S. Dhillon. 5801-5809 [doi]
• Learning Long Term Dependencies via Fourier Recurrent UnitsJiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon. 5810-5818 [doi]
• Tropical Geometry of Deep Neural NetworksLiwen Zhang, Gregory Naitzat, Lek-Heng Lim. 5819-5827 [doi]
• Deep Bayesian Nonparametric TrackingAonan Zhang, John Paisley. 5828-5836 [doi]
• Composable Planning with AttributesAmy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus. 5837-5846 [doi]
• Noisy Natural Gradient as Variational InferenceGuodong Zhang, Shengyang Sun, David K. Duvenaud, Roger B. Grosse. 5847-5856 [doi]
• A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix RecoveryXiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu. 5857-5866 [doi]
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsKaiqing Zhang, Zhuoran Yang, Han Liu 0001, Tong Zhang 0001, Tamer Basar. 5867-5876 [doi]
• Dynamic Regret of Strongly Adaptive MethodsLijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou. 5877-5886 [doi]
• Inter and Intra Topic Structure Learning with Word EmbeddingsHe Zhao, Lan Du, Wray L. Buntine, Mingyuan Zhou. 5887-5896 [doi]
• Adversarially Regularized AutoencodersJunbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun. 5897-5906 [doi]
• MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot LearningBo Zhao, Xinwei Sun 0001, Yanwei Fu, Yuan Yao, Yizhou Wang. 5907-5916 [doi]
• Composite Marginal Likelihood Methods for Random Utility ModelsZhibing Zhao, Lirong Xia. 5917-5926 [doi]
• Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite DataShuai Zheng 0004, James Tin-Yau Kwok. 5927-5935 [doi]
• A Robust Approach to Sequential Information Theoretic PlanningSue Zheng, Jason Pacheco, John W. Fisher III. 5936-5944 [doi]
• Revealing Common Statistical Behaviors in Heterogeneous PopulationsAndrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli. 5945-5954 [doi]
• Understanding Generalization and Optimization Performance of Deep CNNsPan Zhou, Jiashi Feng. 5955-5964 [doi]
• Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei. 5965-5974 [doi]
• A Simple Stochastic Variance Reduced Algorithm with Fast Convergence RatesKaiwen Zhou, Fanhua Shang, James Cheng. 5975-5984 [doi]
• Stochastic Variance-Reduced Cubic Regularized Newton MethodDongruo Zhou, Pan Xu, Quanquan Gu. 5985-5994 [doi]
• Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate PriorsYichi Zhou, Jun Zhu 0001, Jingwei Zhuo. 5995-6003 [doi]
• Distributed Nonparametric Regression under Communication ConstraintsYuancheng Zhu, John Lafferty. 6004-6012 [doi]
• Message Passing Stein Variational Gradient DescentJingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu 0001, Ning Chen, Bo Zhang 0010. 6013-6022 [doi]
• Stochastic Variance-Reduced Hamilton Monte Carlo MethodsDifan Zou, Pan Xu, Quanquan Gu. 6023-6032 [doi]
• Hierarchical Long-term Video Prediction without SupervisionNevan Wichers, Ruben Villegas, Dumitru Erhan, Honglak Lee. 6033-6041 [doi]