Abstract is missing.
- Uncovering Causality from Multivariate Hawkes Integrated CumulantsMassil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy. 1-10 [doi]
- A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete DistributionsJayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh. 11-21 [doi]
- Constrained Policy OptimizationJoshua Achiam, David Held, Aviv Tamar, Pieter Abbeel. 22-31 [doi]
- The Price of Differential Privacy for Online LearningNaman Agarwal, Karan Singh. 32-40 [doi]
- Local Bayesian Optimization of Motor SkillsRiad Akrour, Dmitry Sorokin, Jan Peters 0001, Gerhard Neumann. 41-50 [doi]
- Connected Subgraph Detection with Mirror Descent on SDPsCem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama. 51-59 [doi]
- Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk PrognosisAhmed M. Alaa, Scott Hu, Mihaela van der Schaar. 60-69 [doi]
- A Semismooth Newton Method for Fast, Generic Convex ProgrammingAlnur Ali, Eric Wong, J. Zico Kolter. 70-79 [doi]
- Learning Continuous Semantic Representations of Symbolic ExpressionsMiltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles A. Sutton. 80-88 [doi]
- Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex ParameterZeyuan Allen Zhu. 89-97 [doi]
- Doubly Accelerated Methods for Faster CCA and Generalized EigendecompositionZeyuan Allen Zhu, Yuanzhi Li. 98-106 [doi]
- Faster Principal Component Regression and Stable Matrix Chebyshev ApproximationZeyuan Allen Zhu, Yuanzhi Li. 107-115 [doi]
- Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWUZeyuan Allen Zhu, Yuanzhi Li. 116-125 [doi]
- Near-Optimal Design of Experiments via Regret MinimizationZeyuan Allen Zhu, Yuanzhi Li, Aarti Singh, Yining Wang. 126-135 [doi]
- OptNet: Differentiable Optimization as a Layer in Neural NetworksBrandon Amos, J. Zico Kolter. 136-145 [doi]
- Input Convex Neural NetworksBrandon Amos, Lei Xu, J. Zico Kolter. 146-155 [doi]
- An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank ApproximationDavid G. Anderson, Ming Gu. 156-165 [doi]
- Modular Multitask Reinforcement Learning with Policy SketchesJacob Andreas, Dan Klein, Sergey Levine. 166-175 [doi]
- Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement LearningOron Anschel, Nir Baram, Nahum Shimkin. 176-185 [doi]
- A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical ProficiencyRon Appel, Pietro Perona. 186-194 [doi]
- Deep Voice: Real-time Neural Text-to-SpeechSercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi. 195-204 [doi]
- Oracle Complexity of Second-Order Methods for Finite-Sum ProblemsYossi Arjevani, Ohad Shamir. 205-213 [doi]
- Wasserstein Generative Adversarial NetworksMartín Arjovsky, Soumith Chintala, Léon Bottou. 214-223 [doi]
- Generalization and Equilibrium in Generative Adversarial Nets (GANs)Sanjeev Arora, Rong Ge 0001, Yingyu Liang, Tengyu Ma, Yi Zhang. 224-232 [doi]
- A Closer Look at Memorization in Deep NetworksDevansh Arpit, Stanislaw K. Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien. 233-242 [doi]
- An Alternative Softmax Operator for Reinforcement LearningKavosh Asadi, Michael L. Littman. 243-252 [doi]
- Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical GuaranteesHaim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh. 253-262 [doi]
- Minimax Regret Bounds for Reinforcement LearningMohammad Gheshlaghi Azar, Ian Osband, Rémi Munos. 263-272 [doi]
- Learning the Structure of Generative Models without Labeled DataStephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré. 273-282 [doi]
- Uniform Deviation Bounds for k-Means ClusteringOlivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause 0001. 283-291 [doi]
- Distributed and Provably Good Seedings for k-Means in Constant RoundsOlivier Bachem, Mario Lucic, Andreas Krause 0001. 292-300 [doi]
- Learning Algorithms for Active LearningPhilip Bachman, Alessandro Sordoni, Adam Trischler. 301-310 [doi]
- Improving Viterbi is Hard: Better Runtimes Imply Faster Clique AlgorithmsArturs Backurs, Christos Tzamos. 311-321 [doi]
- Differentially Private Clustering in High-Dimensional Euclidean SpacesMaria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang. 322-331 [doi]
- Strongly-Typed Agents are Guaranteed to Interact SafelyDavid Balduzzi. 332-341 [doi]
- The Shattered Gradients Problem: If resnets are the answer, then what is the question?David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams. 342-350 [doi]
- Neural Taylor Approximations: Convergence and Exploration in Rectifier NetworksDavid Balduzzi, Brian McWilliams, Tony Butler-Yeoman. 351-360 [doi]
- Spectral Learning from a Single Trajectory under Finite-State PoliciesBorja Balle, Odalric-Ambrym Maillard. 361-370 [doi]
- Lost Relatives of the Gumbel TrickMatej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. 371-379 [doi]
- Dynamic Word EmbeddingsRobert Bamler, Stephan Mandt. 380-389 [doi]
- End-to-End Differentiable Adversarial Imitation LearningNir Baram, Oron Anschel, Itai Caspi, Shie Mannor. 390-399 [doi]
- Emulating the Expert: Inverse Optimization through Online LearningAndreas Bärmann, Sebastian Pokutta, Oskar Schneider. 400-410 [doi]
- Unimodal Probability Distributions for Deep Ordinal ClassificationChristopher Beckham, Christopher J. Pal. 411-419 [doi]
- Globally Induced Forest: A Prepruning Compression SchemeJean-Michel Begon, Arnaud Joly, Pierre Geurts. 420-428 [doi]
- End-to-End Learning for Structured Prediction Energy NetworksDavid Belanger, Bishan Yang, Andrew McCallum. 429-439 [doi]
- Learning to Discover Sparse Graphical ModelsEugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko. 440-448 [doi]
- A Distributional Perspective on Reinforcement LearningMarc G. Bellemare, Will Dabney, Rémi Munos. 449-458 [doi]
- Neural Optimizer Search with Reinforcement LearningIrwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le. 459-468 [doi]
- Learning Texture Manifolds with the Periodic Spatial GANUrs Bergmann, Nikolay Jetchev, Roland Vollgraf. 469-477 [doi]
- Differentially Private Learning of Undirected Graphical Models Using Collective Graphical ModelsGarrett Bernstein, Ryan Mckenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau. 478-487 [doi]
- Efficient Online Bandit Multiclass Learning with Õ(√T) RegretAlina Beygelzimer, Francesco Orabona, Chicheng Zhang. 488-497 [doi]
- Guarantees for Greedy Maximization of Non-submodular Functions with ApplicationsAndrew An Bian, Joachim M. Buhmann, Andreas Krause 0001, Sebastian Tschiatschek. 498-507 [doi]
- Robust Submodular Maximization: A Non-Uniform Partitioning ApproachIlija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher. 508-516 [doi]
- Unsupervised Learning by Predicting NoisePiotr Bojanowski, Armand Joulin. 517-526 [doi]
- Adaptive Neural Networks for Efficient InferenceTolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama. 527-536 [doi]
- Compressed Sensing using Generative ModelsAshish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis. 537-546 [doi]
- Programming with a Differentiable Forth InterpreterMatko Bosnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel. 547-556 [doi]
- Practical Gauss-Newton Optimisation for Deep LearningAleksandar Botev, Hippolyt Ritter, David Barber. 557-565 [doi]
- Lazifying Conditional Gradient AlgorithmsGábor Braun, Sebastian Pokutta, Daniel Zink. 566-575 [doi]
- Clustering High Dimensional Dynamic Data StreamsVladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang. 576-585 [doi]
- On the Sampling Problem for Kernel QuadratureFrançois-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami. 586-595 [doi]
- Reduced Space and Faster Convergence in Imperfect-Information Games via PruningNoam Brown, Tuomas Sandholm. 596-604 [doi]
- Globally Optimal Gradient Descent for a ConvNet with Gaussian InputsAlon Brutzkus, Amir Globerson. 605-614 [doi]
- Deep Tensor Convolution on MulticoresDavid M. Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit. 615-624 [doi]
- Multi-objective Bandits: Optimizing the Generalized Gini IndexRóbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor. 625-634 [doi]
- Priv'IT: Private and Sample Efficient Identity TestingBryan Cai, Constantinos Daskalakis, Gautam Kamath. 635-644 [doi]
- Second-Order Kernel Online Convex Optimization with Adaptive SketchingDaniele Calandriello, Alessandro Lazaric, Michal Valko. 645-653 [doi]
- "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex FunctionsYair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford. 654-663 [doi]
- Sliced Wasserstein Kernel for Persistence DiagramsMathieu Carrière, Marco Cuturi, Steve Oudot. 664-673 [doi]
- Multiple Clustering Views from Multiple Uncertain ExpertsYale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy. 674-683 [doi]
- Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal InferenceAditya Chaudhry, Pan Xu, Quanquan Gu. 684-693 [doi]
- Active Heteroscedastic RegressionKamalika Chaudhuri, Prateek Jain 0002, Nagarajan Natarajan. 694-702 [doi]
- Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement LearningYevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine. 703-711 [doi]
- Robust Structured Estimation with Single-Index ModelsSheng Chen, Arindam Banerjee. 712-721 [doi]
- Adaptive Multiple-Arm IdentificationJiecao Chen, Xi Chen, Qin Zhang 0001, Yuan Zhou. 722-730 [doi]
- Dueling Bandits with Weak RegretBangrui Chen, Peter I. Frazier. 731-739 [doi]
- Strong NP-Hardness for Sparse Optimization with Concave Penalty FunctionsYichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, Hao Yin. 740-747 [doi]
- Learning to Learn without Gradient Descent by Gradient DescentYutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew Botvinick, Nando de Freitas. 748-756 [doi]
- Identification and Model Testing in Linear Structural Equation Models using Auxiliary VariablesBryant Chen, Daniel Kumor, Elias Bareinboim. 757-766 [doi]
- Toward Efficient and Accurate Covariance Matrix Estimation on Compressed DataXixian Chen, Michael R. Lyu, Irwin King. 767-776 [doi]
- Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and ScalabilityZhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao. 777-786 [doi]
- Learning to Aggregate Ordinal Labels by Maximizing Separating WidthGuangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng-Ann Heng. 787-796 [doi]
- Nearly Optimal Robust Matrix CompletionYeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain 0002. 797-805 [doi]
- Algorithms for $\ell_p$ Low-Rank ApproximationFlavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar 0001, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff. 806-814 [doi]
- MEC: Memory-efficient Convolution for Deep Neural NetworkMinsik Cho, Daniel Brand. 815-824 [doi]
- On Relaxing Determinism in Arithmetic CircuitsArthur Choi, Adnan Darwiche. 825-833 [doi]
- Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta DistributionPo-wei Chou, Daniel Maturana, Sebastian Scherer. 834-843 [doi]
- On Kernelized Multi-armed BanditsSayak Ray Chowdhury, Aditya Gopalan. 844-853 [doi]
- Parseval Networks: Improving Robustness to Adversarial ExamplesMoustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier. 854-863 [doi]
- Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMCYulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou. 864-873 [doi]
- AdaNet: Adaptive Structural Learning of Artificial Neural NetworksCorinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang. 874-883 [doi]
- Random Feature Expansions for Deep Gaussian ProcessesKurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone. 884-893 [doi]
- Soft-DTW: a Differentiable Loss Function for Time-SeriesMarco Cuturi, Mathieu Blondel. 894-903 [doi]
- Understanding Synthetic Gradients and Decoupled Neural InterfacesWojciech Marian Czarnecki, Grzegorz Swirszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu. 904-912 [doi]
- Stochastic Generative HashingBo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song. 913-922 [doi]
- Logarithmic Time One-Against-SomeHal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro. 923-932 [doi]
- Language Modeling with Gated Convolutional NetworksYann N. Dauphin, Angela Fan, Michael Auli, David Grangier. 933-941 [doi]
- An Infinite Hidden Markov Model With Similarity-Biased TransitionsColin Reimer Dawson, Chaofan Huang, Clayton T. Morrison. 942-950 [doi]
- Distributed Batch Gaussian Process OptimizationErik A. Daxberger, Bryan Kian Hsiang Low. 951-960 [doi]
- Consistency Analysis for Binary Classification RevisitedKrzysztof Dembczynski, Wojciech Kotlowski, Oluwasanmi Koyejo, Nagarajan Natarajan. 961-969 [doi]
- iSurvive: An Interpretable, Event-time Prediction Model for mHealthWalter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg. 970-979 [doi]
- Image-to-Markup Generation with Coarse-to-Fine AttentionYuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush. 980-989 [doi]
- RobustFill: Neural Program Learning under Noisy I/OJacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli. 990-998 [doi]
- Being Robust (in High Dimensions) Can Be PracticalIlias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li 0001, Ankur Moitra, Alistair Stewart. 999-1008 [doi]
- Probabilistic Path Hamiltonian Monte CarloVu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV. 1009-1018 [doi]
- Sharp Minima Can Generalize For Deep NetsLaurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio. 1019-1028 [doi]
- A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVIJustin Domke. 1029-1038 [doi]
- Dance Dance ConvolutionChris Donahue, Zachary C. Lipton, Julian McAuley. 1039-1048 [doi]
- Stochastic Variance Reduction Methods for Policy EvaluationSimon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou. 1049-1058 [doi]
- Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum AgreementJonathan Eckstein, Noam Goldberg, Ai Kagawa. 1059-1067 [doi]
- Neural Audio Synthesis of Musical Notes with WaveNet AutoencodersJesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan. 1068-1077 [doi]
- Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent CoarseningMohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso. 1078-1087 [doi]
- Maximum Selection and Ranking under Noisy ComparisonsMoein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh. 1088-1096 [doi]
- Fake News Mitigation via Point Process Based InterventionMehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias B. Khalil, Shuang Li, Le Song, Hongyuan Zha. 1097-1106 [doi]
- Regret Minimization in Behaviorally-Constrained Zero-Sum GamesGabriele Farina, Christian Kroer, Tuomas Sandholm. 1107-1116 [doi]
- Coresets for Vector Summarization with Applications to Network GraphsDan Feldman, Sedat Ozer, Daniela Rus. 1117-1125 [doi]
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksChelsea Finn, Pieter Abbeel, Sergey Levine. 1126-1135 [doi]
- Input Switched Affine Networks: An RNN Architecture Designed for InterpretabilityJakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo. 1136-1145 [doi]
- Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningJakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson. 1146-1155 [doi]
- Counterfactual Data-Fusion for Online Reinforcement LearnersAndrew Forney, Judea Pearl, Elias Bareinboim. 1156-1164 [doi]
- Forward and Reverse Gradient-Based Hyperparameter OptimizationLuca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil. 1165-1173 [doi]
- Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierJoseph Futoma, Sanjay Hariharan, Katherine A. Heller. 1174-1182 [doi]
- Deep Bayesian Active Learning with Image DataYarin Gal, Riashat Islam, Zoubin Ghahramani. 1183-1192 [doi]
- Local-to-Global Bayesian Network Structure LearningTian Gao, Kshitij P. Fadnis, Murray Campbell. 1193-1202 [doi]
- Communication-efficient Algorithms for Distributed Stochastic Principal Component AnalysisDan Garber, Ohad Shamir, Nathan Srebro. 1203-1212 [doi]
- Differentiable Programs with Neural LibrariesAlexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow. 1213-1222 [doi]
- Zonotope Hit-and-run for Efficient Sampling from Projection DPPsGuillaume Gautier, Rémi Bardenet, Michal Valko. 1223-1232 [doi]
- No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric AnalysisRong Ge 0001, Chi Jin, Yi Zheng. 1233-1242 [doi]
- Convolutional Sequence to Sequence LearningJonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin. 1243-1252 [doi]
- On Context-Dependent Clustering of BanditsClaudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, Evans Etrue. 1253-1262 [doi]
- Neural Message Passing for Quantum ChemistryJustin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl. 1263-1272 [doi]
- Convex Phase Retrieval without Lifting via PhaseMaxTom Goldstein, Christoph Studer. 1273-1281 [doi]
- Preferential Bayesian OptimizationJavier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence. 1282-1291 [doi]
- Measuring Sample Quality with KernelsJackson Gorham, Lester W. Mackey. 1292-1301 [doi]
- Efficient softmax approximation for GPUsGrave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou. 1302-1310 [doi]
- Automated Curriculum Learning for Neural NetworksAlex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu. 1311-1320 [doi]
- On Calibration of Modern Neural NetworksChuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger. 1321-1330 [doi]
- ProtoNN: Compressed and Accurate kNN for Resource-scarce DevicesChirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain 0002. 1331-1340 [doi]
- Deep Value Networks Learn to Evaluate and Iteratively Refine Structured OutputsMichael Gygli, Mohammad Norouzi, Anelia Angelova. 1341-1351 [doi]
- Reinforcement Learning with Deep Energy-Based PoliciesTuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine. 1352-1361 [doi]
- DeepBach: a Steerable Model for Bach Chorales GenerationGaëtan Hadjeres, François Pachet, Frank Nielsen. 1362-1371 [doi]
- Consistent On-Line Off-Policy EvaluationAssaf Hallak, Shie Mannor. 1372-1383 [doi]
- Faster Greedy MAP Inference for Determinantal Point ProcessesInsu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin. 1384-1393 [doi]
- Data-Efficient Policy Evaluation Through Behavior Policy SearchJosiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum. 1394-1403 [doi]
- Joint Dimensionality Reduction and Metric Learning: A Geometric TakeMehrtash Tafazzoli Harandi, Mathieu Salzmann, Richard I. Hartley. 1404-1413 [doi]
- Deep IV: A Flexible Approach for Counterfactual PredictionJason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy. 1414-1423 [doi]
- Robust Guarantees of Stochastic Greedy AlgorithmsAvinatan Hassidim, Yaron Singer. 1424-1432 [doi]
- Efficient Regret Minimization in Non-Convex GamesElad Hazan, Karan Singh, Cyril Zhang. 1433-1441 [doi]
- Kernelized Support Tensor MachinesLiFang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, LinLin Shen, Philip S. Yu, Ann B. Ragin. 1442-1451 [doi]
- The Sample Complexity of Online One-Class Collaborative FilteringReinhard Heckel, Kannan Ramchandran. 1452-1460 [doi]
- Warped Convolutions: Efficient Invariance to Spatial TransformationsJoão F. Henriques, Andrea Vedaldi. 1461-1469 [doi]
- Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical SpaceJosé Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik. 1470-1479 [doi]
- DARLA: Improving Zero-Shot Transfer in Reinforcement LearningIrina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner. 1480-1490 [doi]
- SPLICE: Fully Tractable Hierarchical Extension of ICA with PoolingJunichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe. 1491-1500 [doi]
- Multilevel Clustering via Wasserstein MeansNhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung. 1501-1509 [doi]
- Learning Deep Latent Gaussian Models with Markov Chain Monte CarloMatthew D. Hoffman. 1510-1519 [doi]
- Minimizing Trust Leaks for Robust Sybil DetectionJános Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz. 1520-1528 [doi]
- Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over NetworksMingyi Hong, Davood Hajinezhad, Ming-Min Zhao. 1529-1538 [doi]
- Analysis and Optimization of Graph Decompositions by Lifted MulticutsAndrea Hornáková, Jan-Hendrik Lange, Bjoern Andres. 1539-1548 [doi]
- Dissipativity Theory for Nesterov's Accelerated MethodBin Hu, Laurent Lessard. 1549-1557 [doi]
- Learning Discrete Representations via Information Maximizing Self-Augmented TrainingWeihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama. 1558-1567 [doi]
- State-Frequency Memory Recurrent Neural NetworksHao Hu, Guo-Jun Qi. 1568-1577 [doi]
- Deep Generative Models for Relational Data with Side InformationChangwei Hu, Piyush Rai, Lawrence Carin. 1578-1586 [doi]
- Toward Controlled Generation of TextZhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing. 1587-1596 [doi]
- Tensor Decomposition with SmoothnessMasaaki Imaizumi, Kohei Hayashi. 1597-1606 [doi]
- Variational Inference for Sparse and Undirected ModelsJohn Ingraham, Debora S. Marks. 1607-1616 [doi]
- Fairness in Reinforcement LearningShahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth. 1617-1626 [doi]
- Decoupled Neural Interfaces using Synthetic GradientsMax Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu. 1627-1635 [doi]
- Scalable Generative Models for Multi-label Learning with Missing LabelsVikas Jain, Nirbhay Modhe, Piyush Rai. 1636-1644 [doi]
- Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-controlNatasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck. 1645-1654 [doi]
- Bayesian Optimization with Tree-structured DependenciesRodolphe Jenatton, Cédric Archambeau, Javier González, Matthias Seeger. 1655-1664 [doi]
- Simultaneous Learning of Trees and Representations for Extreme Classification and Density EstimationYacine Jernite, Anna Choromanska, David Sontag. 1665-1674 [doi]
- From Patches to Images: A Nonparametric Generative ModelGeng Ji, Michael C. Hughes, Erik B. Sudderth. 1675-1683 [doi]
- Density Level Set Estimation on Manifolds with DBSCANHeinrich Jiang. 1684-1693 [doi]
- Uniform Convergence Rates for Kernel Density EstimationHeinrich Jiang. 1694-1703 [doi]
- Contextual Decision Processes with low Bellman rank are PAC-LearnableNan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire. 1704-1713 [doi]
- Efficient Nonmyopic Active SearchShali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett. 1714-1723 [doi]
- How to Escape Saddle Points EfficientlyChi Jin, Rong Ge 0001, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan. 1724-1732 [doi]
- Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNsLi Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic. 1733-1741 [doi]
- An Adaptive Test of Independence with Analytic Kernel EmbeddingsWittawat Jitkrittum, Zoltán Szabó 0001, Arthur Gretton. 1742-1751 [doi]
- StingyCD: Safely Avoiding Wasteful Updates in Coordinate DescentTyler B. Johnson, Carlos Guestrin. 1752-1760 [doi]
- Differentially Private Chi-squared Test by Unit Circle MechanismKazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma. 1761-1770 [doi]
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- The Loss Surface of Deep and Wide Neural NetworksQuynh Nguyen, Matthias Hein. 2603-2612 [doi]
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- Scalable Multi-Class Gaussian Process Classification using Expectation PropagationCarlos Villacampa-Calvo, Daniel Hernández-Lobato. 3550-3559 [doi]
- Learning to Generate Long-term Future via Hierarchical PredictionRuben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee. 3560-3569 [doi]
- On orthogonality and learning recurrent networks with long term dependenciesEugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal. 3570-3578 [doi]
- Fast Bayesian Intensity Estimation for the Permanental ProcessChristian J. Walder, Adrian N. Bishop. 3579-3588 [doi]
- Optimal and Adaptive Off-policy Evaluation in Contextual BanditsYu-Xiang Wang, Alekh Agarwal, Miroslav Dudík. 3589-3597 [doi]
- Capacity Releasing Diffusion for Speed and LocalityDi Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao. 3598-3607 [doi]
- Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model AveragingShusen Wang, Alex Gittens, Michael W. Mahoney. 3608-3616 [doi]
- Robust Gaussian Graphical Model Estimation with Arbitrary CorruptionLingxiao Wang, Quanquan Gu. 3617-3626 [doi]
- Max-value Entropy Search for Efficient Bayesian OptimizationZi Wang, Stefanie Jegelka. 3627-3635 [doi]
- Efficient Distributed Learning with SparsityJialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang 0001. 3636-3645 [doi]
- Robust Probabilistic Modeling with Bayesian Data ReweightingYixin Wang, Alp Kucukelbir, David M. Blei. 3646-3655 [doi]
- Batched High-dimensional Bayesian Optimization via Structural Kernel LearningZi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli. 3656-3664 [doi]
- Tensor Decomposition via Simultaneous Power IterationPo-An Wang, Chi-Jen Lu. 3665-3673 [doi]
- Sequence Modeling via SegmentationsChong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng. 3674-3683 [doi]
- Variational Policy for Guiding Point ProcessesYichen Wang, Grady Williams, Evangelos Theodorou, Le Song. 3684-3693 [doi]
- Exploiting Strong Convexity from Data with Primal-Dual First-Order AlgorithmsJialei Wang, Lin Xiao. 3694-3702 [doi]
- Beyond Filters: Compact Feature Map for Portable Deep ModelYunhe Wang, Chang Xu, Chao Xu 0006, Dacheng Tao. 3703-3711 [doi]
- A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix RecoveryLingxiao Wang, Xiao Zhang, Quanquan Gu. 3712-3721 [doi]
- Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process RegressionPengfei Wei, Ramón Sagarna, Yiping Ke, Yew-Soon Ong, Chi Keong Goh. 3722-3731 [doi]
- Latent Intention Dialogue ModelsTsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve J. Young. 3732-3741 [doi]
- Unifying Task Specification in Reinforcement LearningMartha White. 3742-3750 [doi]
- Learned Optimizers that Scale and GeneralizeOlga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein. 3751-3760 [doi]
- Exact Inference for Integer Latent-Variable ModelsKevin Winner, Debora Sujono, Daniel Sheldon. 3761-3770 [doi]
- Tensor Belief PropagationAndrew Wrigley, Wee Sun Lee, Nan Ye. 3771-3779 [doi]
- A Unified View of Multi-Label Performance MeasuresXi-Zhu Wu, Zhi-Hua Zhou. 3780-3788 [doi]
- Dual Supervised LearningYingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu. 3789-3798 [doi]
- Learning Latent Space Models with Angular ConstraintsPengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing. 3799-3810 [doi]
- Uncorrelation and Evenness: a New Diversity-Promoting RegularizerPengtao Xie, Aarti Singh, Eric P. Xing. 3811-3820 [doi]
- Stochastic Convex Optimization: Faster Local Growth Implies Faster Global ConvergenceYi Xu, Qihang Lin, Tianbao Yang. 3821-3830 [doi]
- Learning Hawkes Processes from Short Doubly-Censored Event SequencesHongteng Xu, Dixin Luo, Hongyuan Zha. 3831-3840 [doi]
- Adaptive Consensus ADMM for Distributed OptimizationZheng Xu 0002, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein. 3841-3850 [doi]
- High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function EstimationZhuoran Yang, Krishnakumar Balasubramanian, Han Liu. 3851-3860 [doi]
- Towards K-means-friendly Spaces: Simultaneous Deep Learning and ClusteringBo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong. 3861-3870 [doi]
- On The Projection Operator to A Three-view Cardinality Constrained SetHaichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu. 3871-3880 [doi]
- Improved Variational Autoencoders for Text Modeling using Dilated ConvolutionsZichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick. 3881-3890 [doi]
- Tensor-Train Recurrent Neural Networks for Video ClassificationYinchong Yang, Denis Krompass, Volker Tresp. 3891-3900 [doi]
- A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved RatesTianbao Yang, Qihang Lin, Lijun Zhang 0005. 3901-3910 [doi]
- Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-ConvexityEunho Yang, Aurélie C. Lozano. 3911-3920 [doi]
- Scalable Bayesian Rule ListsHongyu Yang, Cynthia Rudin, Margo Seltzer. 3921-3930 [doi]
- Approximate Newton Methods and Their Local ConvergenceHaishan Ye, Luo Luo, Zhihua Zhang. 3931-3939 [doi]
- A Simulated Annealing Based Inexact Oracle for Wasserstein Loss MinimizationJianbo Ye, James Ze Wang, Jia Li. 3940-3948 [doi]
- Latent Feature LassoIan En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-de Lin, Pradeep Ravikumar. 3949-3957 [doi]
- Combined Group and Exclusive Sparsity for Deep Neural NetworksJaehong Yoon, Sung Ju Hwang. 3958-3966 [doi]
- Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence DataManzil Zaheer, Amr Ahmed, Alexander J. Smola. 3967-3976 [doi]
- Canopy Fast Sampling with Cover TreesManzil Zaheer, Satwik Kottur, Amr Ahmed, José M. F. Moura, Alexander J. Smola. 3977-3986 [doi]
- Continual Learning Through Synaptic IntelligenceFriedemann Zenke, Ben Poole, Surya Ganguli. 3987-3995 [doi]
- Stochastic Gradient Monomial Gamma SamplerYizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin. 3996-4005 [doi]
- Adversarial Feature Matching for Text GenerationYizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin. 4006-4015 [doi]
- Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample ReductionWeizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang. 4016-4025 [doi]
- Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient MethodChenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T.-H. Hubert Chan. 4026-4034 [doi]
- ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep LearningHantian Zhang, Jerry Li 0001, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang. 4035-4043 [doi]
- Convexified Convolutional Neural NetworksYuchen Zhang, Percy Liang, Martin J. Wainwright. 4044-4053 [doi]
- Projection-free Distributed Online Learning in NetworksWenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang. 4054-4062 [doi]
- Multi-Class Optimal Margin Distribution MachineTeng Zhang, Zhi-Hua Zhou. 4063-4071 [doi]
- Leveraging Node Attributes for Incomplete Relational DataHe Zhao, Lan Du, Wray L. Buntine. 4072-4081 [doi]
- Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement RankLiang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li 0001, Jian Tang, Bo Yuan. 4082-4090 [doi]
- Learning Hierarchical Features from Deep Generative ModelsShengjia Zhao, Jiaming Song, Stefano Ermon. 4091-4099 [doi]
- Learning Sleep Stages from Radio Signals: A Conditional Adversarial ArchitectureMingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi. 4100-4109 [doi]
- Follow the Moving Leader in Deep LearningShuai Zheng 0004, James T. Kwok. 4110-4119 [doi]
- Asynchronous Stochastic Gradient Descent with Delay CompensationShuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu. 4120-4129 [doi]
- Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning PossibleKai Zheng 0007, Wenlong Mou, Liwei Wang. 4130-4139 [doi]
- Recovery Guarantees for One-hidden-layer Neural NetworksKai Zhong, Zhao Song, Prateek Jain 0002, Peter L. Bartlett, Inderjit S. Dhillon. 4140-4149 [doi]
- Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected ValuesChaoxu Zhou, Wenbo Gao, Donald Goldfarb. 4150-4159 [doi]
- Identify the Nash Equilibrium in Static Games with Random PayoffsYichi Zhou, Jialian Li, Jun Zhu. 4160-4169 [doi]
- When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience ApplicationsHao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh. 4170-4179 [doi]
- High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization AlgorithmRongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu. 4180-4188 [doi]
- Recurrent Highway NetworksJulian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber. 4189-4198 [doi]
- Online Learning to Rank in Stochastic Click ModelsMasrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen. 4199-4208 [doi]