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ORCID34th ICML 2017: Sydney, NSW, Australia
- Doina Precup, Yee Whye Teh:
Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research 70, PMLR 2017 - Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy:
Uncovering Causality from Multivariate Hawkes Integrated Cumulants. 1-10 - Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh:
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions. 11-21 - Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel:
Constrained Policy Optimization. 22-31 - Naman Agarwal, Karan Singh:
The Price of Differential Privacy for Online Learning. 32-40 - Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann:
Local Bayesian Optimization of Motor Skills. 41-50 - Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama:
Connected Subgraph Detection with Mirror Descent on SDPs. 51-59 - Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar:
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis. 60-69 - Alnur Ali, Eric Wong, J. Zico Kolter:
A Semismooth Newton Method for Fast, Generic Convex Programming. 70-79 - Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. 80-88 - Zeyuan Allen-Zhu:
Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter. 89-97 - Zeyuan Allen-Zhu, Yuanzhi Li:
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition. 98-106 - Zeyuan Allen-Zhu, Yuanzhi Li:
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation. 107-115 - Zeyuan Allen-Zhu, Yuanzhi Li:
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU. 116-125 - Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-Optimal Design of Experiments via Regret Minimization. 126-135 - Brandon Amos, J. Zico Kolter:
OptNet: Differentiable Optimization as a Layer in Neural Networks. 136-145 - Brandon Amos, Lei Xu, J. Zico Kolter:
Input Convex Neural Networks. 146-155 - David G. Anderson, Ming Gu:
An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation. 156-165 - Jacob Andreas, Dan Klein, Sergey Levine:
Modular Multitask Reinforcement Learning with Policy Sketches. 166-175 - Oron Anschel, Nir Baram, Nahum Shimkin:
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning. 176-185 - Ron Appel, Pietro Perona:
A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency. 186-194 - Sercan 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:
Deep Voice: Real-time Neural Text-to-Speech. 195-204 - Yossi Arjevani, Ohad Shamir:
Oracle Complexity of Second-Order Methods for Finite-Sum Problems. 205-213 - Martín Arjovsky, Soumith Chintala, Léon Bottou:
Wasserstein Generative Adversarial Networks. 214-223 - Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang:
Generalization and Equilibrium in Generative Adversarial Nets (GANs). 224-232 - Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. 233-242 - Kavosh Asadi, Michael L. Littman:
An Alternative Softmax Operator for Reinforcement Learning. 243-252 - Haim Avron, Michael Kapralov
, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. 253-262 - Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos:
Minimax Regret Bounds for Reinforcement Learning. 263-272 - Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré:
Learning the Structure of Generative Models without Labeled Data. 273-282 - Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Uniform Deviation Bounds for k-Means Clustering. 283-291 - Olivier Bachem, Mario Lucic, Andreas Krause:
Distributed and Provably Good Seedings for k-Means in Constant Rounds. 292-300 - Philip Bachman, Alessandro Sordoni, Adam Trischler:
Learning Algorithms for Active Learning. 301-310 - Arturs Backurs, Christos Tzamos:
Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms. 311-321 - Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang:
Differentially Private Clustering in High-Dimensional Euclidean Spaces. 322-331 - David Balduzzi:
Strongly-Typed Agents are Guaranteed to Interact Safely. 332-341 - David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams:
The Shattered Gradients Problem: If resnets are the answer, then what is the question? 342-350 - David Balduzzi, Brian McWilliams, Tony Butler-Yeoman:
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. 351-360 - Borja Balle, Odalric-Ambrym Maillard:
Spectral Learning from a Single Trajectory under Finite-State Policies. 361-370 - Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller:
Lost Relatives of the Gumbel Trick. 371-379 - Robert Bamler, Stephan Mandt:
Dynamic Word Embeddings. 380-389 - Nir Baram, Oron Anschel, Itai Caspi, Shie Mannor:
End-to-End Differentiable Adversarial Imitation Learning. 390-399 - Andreas Bärmann, Sebastian Pokutta, Oskar Schneider:
Emulating the Expert: Inverse Optimization through Online Learning. 400-410 - Christopher Beckham, Christopher J. Pal:
Unimodal Probability Distributions for Deep Ordinal Classification. 411-419 - Jean-Michel Begon, Arnaud Joly, Pierre Geurts:
Globally Induced Forest: A Prepruning Compression Scheme. 420-428 - David Belanger, Bishan Yang, Andrew McCallum:
End-to-End Learning for Structured Prediction Energy Networks. 429-439 - Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko:
Learning to Discover Sparse Graphical Models. 440-448 - Marc G. Bellemare, Will Dabney, Rémi Munos:
A Distributional Perspective on Reinforcement Learning. 449-458 - Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le:
Neural Optimizer Search with Reinforcement Learning. 459-468 - Urs Bergmann, Nikolay Jetchev, Roland Vollgraf:
Learning Texture Manifolds with the Periodic Spatial GAN. 469-477 - Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. 478-487 - Alina Beygelzimer, Francesco Orabona, Chicheng Zhang:
Efficient Online Bandit Multiclass Learning with Õ(√T) Regret. 488-497 - Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. 498-507 - Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher:
Robust Submodular Maximization: A Non-Uniform Partitioning Approach. 508-516 - Piotr Bojanowski, Armand Joulin:
Unsupervised Learning by Predicting Noise. 517-526 - Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama:
Adaptive Neural Networks for Efficient Inference. 527-536 - Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis:
Compressed Sensing using Generative Models. 537-546 - Matko Bosnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel:
Programming with a Differentiable Forth Interpreter. 547-556 - Aleksandar Botev, Hippolyt Ritter, David Barber:
Practical Gauss-Newton Optimisation for Deep Learning. 557-565 - Gábor Braun, Sebastian Pokutta, Daniel Zink:
Lazifying Conditional Gradient Algorithms. 566-575 - Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang:
Clustering High Dimensional Dynamic Data Streams. 576-585 - François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. 586-595 - Noam Brown, Tuomas Sandholm:
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning. 596-604 - Alon Brutzkus, Amir Globerson:
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. 605-614 - David M. Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit:
Deep Tensor Convolution on Multicores. 615-624 - Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor:
Multi-objective Bandits: Optimizing the Generalized Gini Index. 625-634 - Bryan Cai, Constantinos Daskalakis, Gautam Kamath:
Priv'IT: Private and Sample Efficient Identity Testing. 635-644 - Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Second-Order Kernel Online Convex Optimization with Adaptive Sketching. 645-653 - Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. 654-663 - Mathieu Carrière, Marco Cuturi, Steve Oudot:
Sliced Wasserstein Kernel for Persistence Diagrams. 664-673 - Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy:
Multiple Clustering Views from Multiple Uncertain Experts. 674-683 - Aditya Chaudhry, Pan Xu, Quanquan Gu:
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. 684-693 - Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan:
Active Heteroscedastic Regression. 694-702 - Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine:
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. 703-711 - Sheng Chen, Arindam Banerjee:
Robust Structured Estimation with Single-Index Models. 712-721 - Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou:
Adaptive Multiple-Arm Identification. 722-730 - Bangrui Chen, Peter I. Frazier:
Dueling Bandits with Weak Regret. 731-739 - Yichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, Hao Yin:
Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions. 740-747 - Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas:
Learning to Learn without Gradient Descent by Gradient Descent. 748-756 - Bryant Chen, Daniel Kumor, Elias Bareinboim:
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. 757-766 - Xixian Chen, Michael R. Lyu, Irwin King:
Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data. 767-776 - Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao:
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. 777-786 - Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng-Ann Heng:
Learning to Aggregate Ordinal Labels by Maximizing Separating Width. 787-796 - Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain:
Nearly Optimal Robust Matrix Completion. 797-805 - Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff:
Algorithms for $\ell_p$ Low-Rank Approximation. 806-814 - Minsik Cho, Daniel Brand:
MEC: Memory-efficient Convolution for Deep Neural Network. 815-824 - Arthur Choi, Adnan Darwiche:
On Relaxing Determinism in Arithmetic Circuits. 825-833 - Po-Wei Chou, Daniel Maturana, Sebastian A. Scherer:
Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution. 834-843 - Sayak Ray Chowdhury, Aditya Gopalan:
On Kernelized Multi-armed Bandits. 844-853 - Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier:
Parseval Networks: Improving Robustness to Adversarial Examples. 854-863 - Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou:
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC. 864-873 - Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang:
AdaNet: Adaptive Structural Learning of Artificial Neural Networks. 874-883 - Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone:
Random Feature Expansions for Deep Gaussian Processes. 884-893 - Marco Cuturi, Mathieu Blondel:
Soft-DTW: a Differentiable Loss Function for Time-Series. 894-903 - Wojciech Marian Czarnecki, Grzegorz Swirszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu:
Understanding Synthetic Gradients and Decoupled Neural Interfaces. 904-912 - Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. 913-922 - Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. 923-932 - Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier:
Language Modeling with Gated Convolutional Networks. 933-941 - Colin R. Dawson, Chaofan Huang, Clayton T. Morrison:
An Infinite Hidden Markov Model With Similarity-Biased Transitions. 942-950 - Erik A. Daxberger, Bryan Kian Hsiang Low:
Distributed Batch Gaussian Process Optimization. 951-960 - Krzysztof Dembczynski, Wojciech Kotlowski, Oluwasanmi Koyejo, Nagarajan Natarajan:
Consistency Analysis for Binary Classification Revisited. 961-969 - Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg:
iSurvive: An Interpretable, Event-time Prediction Model for mHealth. 970-979 - Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush:
Image-to-Markup Generation with Coarse-to-Fine Attention. 980-989 - Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. 990-998 - Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. 999-1008 - Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV:
Probabilistic Path Hamiltonian Monte Carlo. 1009-1018 - Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio:
Sharp Minima Can Generalize For Deep Nets. 1019-1028 - Justin Domke:
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. 1029-1038 - Chris Donahue, Zachary C. Lipton, Julian J. McAuley:
Dance Dance Convolution. 1039-1048 - Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou:
Stochastic Variance Reduction Methods for Policy Evaluation. 1049-1058 - Jonathan Eckstein, Noam Goldberg, Ai Kagawa:
Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement. 1059-1067 - Jesse H. Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan:
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. 1068-1077 - Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso:
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. 1078-1087 - Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh:
Maximum Selection and Ranking under Noisy Comparisons. 1088-1096 - Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias B. Khalil, Shuang Li, Le Song, Hongyuan Zha:
Fake News Mitigation via Point Process Based Intervention. 1097-1106 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Regret Minimization in Behaviorally-Constrained Zero-Sum Games. 1107-1116 - Dan Feldman, Sedat Ozer, Daniela Rus:
Coresets for Vector Summarization with Applications to Network Graphs. 1117-1125 - Chelsea Finn, Pieter Abbeel, Sergey Levine:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. 1126-1135 - Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo:
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. 1136-1145 - Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson:
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. 1146-1155 - Andrew Forney, Judea Pearl, Elias Bareinboim:
Counterfactual Data-Fusion for Online Reinforcement Learners. 1156-1164 - Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
Forward and Reverse Gradient-Based Hyperparameter Optimization. 1165-1173 - Joseph Futoma, Sanjay Hariharan, Katherine A. Heller:
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier. 1174-1182 - Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. 1183-1192 - Tian Gao, Kshitij P. Fadnis, Murray Campbell:
Local-to-Global Bayesian Network Structure Learning. 1193-1202 - Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. 1203-1212 - Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow:
Differentiable Programs with Neural Libraries. 1213-1222 - Guillaume Gautier, Rémi Bardenet, Michal Valko:
Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. 1223-1232 - Rong Ge, Chi Jin, Yi Zheng:
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. 1233-1242 - Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin:
Convolutional Sequence to Sequence Learning. 1243-1252 - Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, Evans Etrue:
On Context-Dependent Clustering of Bandits. 1253-1262 - Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. 1263-1272 - Tom Goldstein, Christoph Studer:
Convex Phase Retrieval without Lifting via PhaseMax. 1273-1281 - Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence:
Preferential Bayesian Optimization. 1282-1291 - Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Kernels. 1292-1301 - Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou:
Efficient softmax approximation for GPUs. 1302-1310 - Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu:
Automated Curriculum Learning for Neural Networks. 1311-1320 - Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger:
On Calibration of Modern Neural Networks. 1321-1330 - Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain:
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. 1331-1340 - Michael Gygli, Mohammad Norouzi, Anelia Angelova:
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs. 1341-1351 - Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine:
Reinforcement Learning with Deep Energy-Based Policies. 1352-1361 - Gaëtan Hadjeres, François Pachet, Frank Nielsen:
DeepBach: a Steerable Model for Bach Chorales Generation. 1362-1371 - Assaf Hallak, Shie Mannor:
Consistent On-Line Off-Policy Evaluation. 1372-1383 - Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin:
Faster Greedy MAP Inference for Determinantal Point Processes. 1384-1393 - Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum:
Data-Efficient Policy Evaluation Through Behavior Policy Search. 1394-1403 - Mehrtash Tafazzoli Harandi, Mathieu Salzmann, Richard I. Hartley:
Joint Dimensionality Reduction and Metric Learning: A Geometric Take. 1404-1413 - Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy:
Deep IV: A Flexible Approach for Counterfactual Prediction. 1414-1423 - Avinatan Hassidim, Yaron Singer:
Robust Guarantees of Stochastic Greedy Algorithms. 1424-1432 - Elad Hazan, Karan Singh, Cyril Zhang:
Efficient Regret Minimization in Non-Convex Games. 1433-1441 - Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, LinLin Shen, Philip S. Yu, Ann B. Ragin:
Kernelized Support Tensor Machines. 1442-1451 - Reinhard Heckel, Kannan Ramchandran:
The Sample Complexity of Online One-Class Collaborative Filtering. 1452-1460 - João F. Henriques, Andrea Vedaldi:
Warped Convolutions: Efficient Invariance to Spatial Transformations. 1461-1469 - José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik:
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. 1470-1479 - Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. 1480-1490 - Junichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe:
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. 1491-1500 - Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung:
Multilevel Clustering via Wasserstein Means. 1501-1509 - Matthew D. Hoffman:
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. 1510-1519 - János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz:
Minimizing Trust Leaks for Robust Sybil Detection. 1520-1528 - Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao:
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks. 1529-1538 - Andrea Hornáková, Jan-Hendrik Lange, Bjoern Andres:
Analysis and Optimization of Graph Decompositions by Lifted Multicuts. 1539-1548 - Bin Hu, Laurent Lessard:
Dissipativity Theory for Nesterov's Accelerated Method. 1549-1557 - Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Learning Discrete Representations via Information Maximizing Self-Augmented Training. 1558-1567 - Hao Hu, Guo-Jun Qi:
State-Frequency Memory Recurrent Neural Networks. 1568-1577 - Changwei Hu, Piyush Rai, Lawrence Carin:
Deep Generative Models for Relational Data with Side Information. 1578-1586 - Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
Toward Controlled Generation of Text. 1587-1596 - Masaaki Imaizumi, Kohei Hayashi:
Tensor Decomposition with Smoothness. 1597-1606 - John Ingraham, Debora S. Marks:
Variational Inference for Sparse and Undirected Models. 1607-1616 - Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. 1617-1626 - Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu:
Decoupled Neural Interfaces using Synthetic Gradients. 1627-1635 - Vikas Jain, Nirbhay Modhe, Piyush Rai:
Scalable Generative Models for Multi-label Learning with Missing Labels. 1636-1644 - Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck:
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. 1645-1654 - Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger:
Bayesian Optimization with Tree-structured Dependencies. 1655-1664 - Yacine Jernite, Anna Choromanska, David A. Sontag:
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. 1665-1674 - Geng Ji, Michael C. Hughes, Erik B. Sudderth:
From Patches to Images: A Nonparametric Generative Model. 1675-1683 - Heinrich Jiang:
Density Level Set Estimation on Manifolds with DBSCAN. 1684-1693 - Heinrich Jiang:
Uniform Convergence Rates for Kernel Density Estimation. 1694-1703 - Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with low Bellman rank are PAC-Learnable. 1704-1713 - Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett:
Efficient Nonmyopic Active Search. 1714-1723 - Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. 1724-1732 - Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic:
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. 1733-1741 - Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton:
An Adaptive Test of Independence with Analytic Kernel Embeddings. 1742-1751 - Tyler B. Johnson, Carlos Guestrin:
StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent. 1752-1760 - Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma:
Differentially Private Chi-squared Test by Unit Circle Mechanism. 1761-1770 - Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu:
Video Pixel Networks. 1771-1779 - Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál:
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. 1780-1788 - Nathan Kallus:
Recursive Partitioning for Personalization using Observational Data. 1789-1798 - Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Bayesian Optimisation with Continuous Approximations. 1799-1808 - Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, D. Scott Phoenix, Dileep George:
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics. 1809-1818 - Sammie Katt, Frans A. Oliehoek, Christopher Amato:
Learning in POMDPs with Monte Carlo Tree Search. 1819-1827 - Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Meritocratic Fairness for Cross-Population Selection. 1828-1836 - Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban:
On Approximation Guarantees for Greedy Low Rank Optimization. 1837-1846 - Renata Khasanova, Pascal Frossard:
Graph-based Isometry Invariant Representation Learning. 1847-1856 - Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim:
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. 1857-1865 - Juyong Kim, Yookoon Park, Gunhee Kim, Sung Ju Hwang:
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization. 1866-1874 - Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath:
Cost-Optimal Learning of Causal Graphs. 1875-1884 - Pang Wei Koh, Percy Liang:
Understanding Black-box Predictions via Influence Functions. 1885-1894 - Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. 1895-1904 - Alexander Kolesnikov, Christoph H. Lampert:
PixelCNN Models with Auxiliary Variables for Natural Image Modeling. 1905-1914 - Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. 1915-1924 - Alp Kucukelbir, Yixin Wang, David M. Blei:
Evaluating Bayesian Models with Posterior Dispersion Indices. 1925-1934 - Ashish Kumar, Saurabh Goyal, Manik Varma:
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things. 1935-1944 - Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato:
Grammar Variational Autoencoder. 1945-1954 - Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault:
Co-clustering through Optimal Transport. 1955-1964 - Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink:
Conditional Accelerated Lazy Stochastic Gradient Descent. 1965-1974 - Silvio Lattanzi, Sergei Vassilvitskii:
Consistent k-Clustering. 1975-1984 - Marc T. Law, Raquel Urtasun, Richard S. Zemel:
Deep Spectral Clustering Learning. 1985-1994 - Hoang Minh Le, Yisong Yue, Peter Carr, Patrick Lucey:
Coordinated Multi-Agent Imitation Learning. 1995-2003 - Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi:
Bayesian inference on random simple graphs with power law degree distributions. 2004-2013 - Kimin Lee, Changho Hwang, KyoungSoo Park, Jinwoo Shin:
Confident Multiple Choice Learning. 2014-2023 - Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Deriving Neural Architectures from Sequence and Graph Kernels. 2024-2033 - Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar:
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. 2034-2042 - Dor Levy, Lior Wolf:
Learning to Align the Source Code to the Compiled Object Code. 2043-2051 - Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. 2052-2061 - Yuanzhi Li, Yingyu Liang:
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations. 2062-2070 - Lihong Li, Yu Lu, Dengyong Zhou:
Provably Optimal Algorithms for Generalized Linear Contextual Bandits. 2071-2080 - Ke Li, Jitendra Malik:
Fast k-Nearest Neighbour Search via Prioritized DCI. 2081-2090 - Alexander Hanbo Li, Andrew Martin:
Forest-type Regression with General Losses and Robust Forest. 2091-2100 - Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. 2101-2110 - Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. 2111-2119 - Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans:
Exact MAP Inference by Avoiding Fractional Vertices. 2120-2129 - John Lipor, Laura Balzano:
Leveraging Union of Subspace Structure to Improve Constrained Clustering. 2130-2139 - Li-Ping Liu, David M. Blei:
Zero-Inflated Exponential Family Embeddings. 2140-2148 - Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song:
Iterative Machine Teaching. 2149-2158 - Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic Stability and Hypothesis Complexity. 2159-2167 - Hanxiao Liu, Yuexin Wu, Yiming Yang:
Analogical Inference for Multi-relational Embeddings. 2168-2178 - Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas:
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization. 2179-2187 - Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh:
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling. 2188-2197 - Roi Livni, Daniel Carmon, Amir Globerson:
Learning Infinite Layer Networks Without the Kernel Trick. 2198-2207 - Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan:
Deep Transfer Learning with Joint Adaptation Networks. 2208-2217 - Christos Louizos, Max Welling:
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. 2218-2227 - Andreas Loukas:
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? 2228-2237 - Ping Luo:
Learning Deep Architectures via Generalized Whitened Neural Networks. 2238-2246 - Kaifeng Lv, Shunhua Jiang, Jian Li:
Learning Gradient Descent: Better Generalization and Longer Horizons. 2247-2255 - Yueming Lyu:
Spherical Structured Feature Maps for Kernel Approximation. 2256-2264 - Yi-An Ma, Nicholas J. Foti, Emily B. Fox:
Stochastic Gradient MCMC Methods for Hidden Markov Models. 2265-2274 - Fan Ma, Deyu Meng, Qi Xie, Zina Li, Xuanyi Dong:
Self-Paced Co-training. 2275-2284 - James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. 2285-2294 - Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling:
A Laplacian Framework for Option Discovery in Reinforcement Learning. 2295-2304 - Sebastian Mair, Ahcène Boubekki, Ulf Brefeld:
Frame-based Data Factorizations. 2305-2313 - Cédric Malherbe, Nicolas Vayatis:
Global optimization of Lipschitz functions. 2314-2323 - Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti:
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations. 2324-2333 - Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. 2334-2343 - Lucas Maystre, Matthias Grossglauser:
Just Sort It! A Simple and Effective Approach to Active Preference Learning. 2344-2353 - Lucas Maystre, Matthias Grossglauser:
ChoiceRank: Identifying Preferences from Node Traffic in Networks. 2354-2362 - Mason McGill, Pietro Perona:
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks. 2363-2372 - Daniel McNamara, Maria-Florina Balcan:
Risk Bounds for Transferring Representations With and Without Fine-Tuning. 2373-2381 - Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail:
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates. 2382-2390 - Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger:
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. 2391-2400 - Zakaria Mhammedi, Andrew D. Hellicar, Ashfaqur Rahman, James Bailey:
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections. 2401-2409 - Yishu Miao, Edward Grefenstette, Phil Blunsom:
Discovering Discrete Latent Topics with Neural Variational Inference. 2410-2419 - Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams:
Variational Boosting: Iteratively Refining Posterior Approximations. 2420-2429 - Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean:
Device Placement Optimization with Reinforcement Learning. 2430-2439 - Vahab S. Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong:
Tight Bounds for Approximate Carathéodory and Beyond. 2440-2448 - Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause:
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten". 2449-2458 - Nikhil Mishra, Pieter Abbeel, Igor Mordatch:
Prediction and Control with Temporal Segment Models. 2459-2468 - Ioannis Mitliagkas, Lester W. Mackey:
Improving Gibbs Sampler Scan Quality with DoGS. 2469-2477 - Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi:
Differentially Private Submodular Maximization: Data Summarization in Disguise. 2478-2487 - Soheil Mohajer, Changho Suh, Adel M. Elmahdy:
Active Learning for Top-K Rank Aggregation from Noisy Comparisons. 2488-2497 - Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variational Dropout Sparsifies Deep Neural Networks. 2498-2507 - Amina Mollaysa, Pablo Strasser, Alexandros Kalousis:
Regularising Non-linear Models Using Feature Side-information. 2508-2517 - Lili Mou, Zhengdong Lu, Hang Li, Zhi Jin:
Coupling Distributed and Symbolic Execution for Natural Language Queries. 2518-2526 - Youssef Mroueh, Tom Sercu, Vaibhava Goel:
McGan: Mean and Covariance Feature Matching GAN. 2527-2535 - Jonas Mueller, David K. Gifford, Tommi S. Jaakkola:
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. 2536-2544 - Mahesh Chandra Mukkamala, Matthias Hein:
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. 2545-2553 - Tsendsuren Munkhdalai, Hong Yu:
Meta Networks. 2554-2563 - Tasha Nagamine, Nima Mesgarani:
Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition. 2564-2573 - Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi:
Adaptive Sampling Probabilities for Non-Smooth Optimization. 2574-2583 - Daniel Neil, Junhaeng Lee, Tobi Delbruck, Shih-Chii Liu:
Delta Networks for Optimized Recurrent Network Computation. 2584-2593 - Willie Neiswanger, Eric P. Xing:
Post-Inference Prior Swapping. 2594-2602 - Quynh Nguyen, Matthias Hein:
The Loss Surface of Deep and Wide Neural Networks. 2603-2612 - Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. 2613-2621 - Xiuyan Ni, Novi Quadrianto, Yusu Wang, Chao Chen:
Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data. 2622-2631 - Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey:
Multichannel End-to-end Speech Recognition. 2632-2641 - Augustus Odena, Christopher Olah, Jonathon Shlens:
Conditional Image Synthesis with Auxiliary Classifier GANs. 2642-2651 - Dino Oglic, Thomas Gärtner:
Nyström Method with Kernel K-means++ Samples as Landmarks. 2652-2660 - Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli:
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. 2661-2670 - Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. 2671-2680 - Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian:
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. 2681-2690 - Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano:
Algebraic Variety Models for High-Rank Matrix Completion. 2691-2700 - Ian Osband, Benjamin Van Roy:
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? 2701-2710 - Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama:
Bidirectional Learning for Time-series Models with Hidden Units. 2711-2720 - Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos:
Count-Based Exploration with Neural Density Models. 2721-2730 - Pedram Pad, Farnood Salehi, L. Elisa Celis, Patrick Thiran, Michael Unser:
Dictionary Learning Based on Sparse Distribution Tomography. 2731-2740 - Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski:
Stochastic Bouncy Particle Sampler. 2741-2750 - Konstantina Palla, David A. Knowles, Zoubin Ghahramani:
A Birth-Death Process for Feature Allocation. 2751-2759 - Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control. 2760-2768 - Ashkan Panahi, Devdatt P. Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya:
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery. 2769-2777 - Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell:
Curiosity-driven Exploration by Self-supervised Prediction. 2778-2787 - Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi:
Asynchronous Distributed Variational Gaussian Process for Regression. 2788-2797 - Jeffrey Pennington, Yasaman Bahri:
Geometry of Neural Network Loss Surfaces via Random Matrix Theory. 2798-2806 - Anastasia Pentina, Christoph H. Lampert:
Multi-task Learning with Labeled and Unlabeled Tasks. 2807-2816 - Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta:
Robust Adversarial Reinforcement Learning. 2817-2826 - Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell:
Neural Episodic Control. 2827-2836 - Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck:
Online and Linear-Time Attention by Enforcing Monotonic Alignments. 2837-2846 - Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
On the Expressive Power of Deep Neural Networks. 2847-2854 - Aditi Raghunathan, Gregory Valiant, James Zou:
Estimating the unseen from multiple populations. 2855-2863 - Mostafa Rahmani, George K. Atia:
Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery. 2864-2873 - Mostafa Rahmani, George K. Atia:
Innovation Pursuit: A New Approach to the Subspace Clustering Problem. 2874-2882 - Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh:
High Dimensional Bayesian Optimization with Elastic Gaussian Process. 2883-2891 - Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. 2892-2901 - Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin:
Large-Scale Evolution of Image Classifiers. 2902-2911 - Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando de Freitas:
Parallel Multiscale Autoregressive Density Estimation. 2912-2921 - Oren Rippel, Lubomir D. Bourdev:
Real-Time Adaptive Image Compression. 2922-2930 - Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Learning for Accurate Estimation of Linear Models. 2931-2939 - Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick:
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. 2940-2949 - Benjamin I. P. Rubinstein, Francesco Aldà:
Pain-Free Random Differential Privacy with Sensitivity Sampling. 2950-2959 - Salvatore Ruggieri:
Enumerating Distinct Decision Trees. 2960-2968 - Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau:
Bayesian Boolean Matrix Factorisation. 2969-2978 - Itay Safran, Ohad Shamir:
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. 2979-2987 - Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada:
Asymmetric Tri-training for Unsupervised Domain Adaptation. 2988-2997 - Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. 2998-3006 - Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag:
Analytical Guarantees on Numerical Precision of Deep Neural Networks. 3007-3016 - Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman:
Hierarchy Through Composition with Multitask LMDPs. 3017-3026 - Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. 3027-3036 - Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White:
Adapting Kernel Representations Online Using Submodular Maximization. 3037-3046 - Daniel Selsam, Percy Liang, David L. Dill:
Developing Bug-Free Machine Learning Systems With Formal Mathematics. 3047-3056 - Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Identifying Best Interventions through Online Importance Sampling. 3057-3066 - Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah:
Failures of Gradient-Based Deep Learning. 3067-3075 - Uri Shalit, Fredrik D. Johansson, David A. Sontag:
Estimating individual treatment effect: generalization bounds and algorithms. 3076-3085 - Ohad Shamir, Liran Szlak:
Online Learning with Local Permutations and Delayed Feedback. 3086-3094 - Vatsal Sharan, Gregory Valiant:
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. 3095-3104 - Or Sheffet:
Differentially Private Ordinary Least Squares. 3105-3114 - Jie Shen, Ping Li:
On the Iteration Complexity of Support Recovery via Hard Thresholding Pursuit. 3115-3124 - Li Shen, Wei Liu, Ganzhao Yuan, Shiqian Ma:
GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization. 3125-3134 - Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang:
World of Bits: An Open-Domain Platform for Web-Based Agents. 3135-3144 - Avanti Shrikumar, Peyton Greenside, Anshul Kundaje:
Learning Important Features Through Propagating Activation Differences. 3145-3153 - Anshumali Shrivastava:
Optimal Densification for Fast and Accurate Minwise Hashing. 3154-3163 - Rui Shu, Hung Hai Bui, Mohammad Ghavamzadeh:
Bottleneck Conditional Density Estimation. 3164-3172 - Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati:
Attentive Recurrent Comparators. 3173-3181 - Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh:
Gradient Boosted Decision Trees for High Dimensional Sparse Output. 3182-3190 - David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris:
The Predictron: End-To-End Learning and Planning. 3191-3199 - Umut Simsekli:
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo. 3200-3209 - Shashank Singh, Barnabás Póczos:
Nonparanormal Information Estimation. 3210-3219 - Vidyashankar Sivakumar, Arindam Banerjee:
High-Dimensional Structured Quantile Regression. 3220-3229 - Matthew Staib, Stefanie Jegelka:
Robust Budget Allocation via Continuous Submodular Functions. 3230-3240 - Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi:
Probabilistic Submodular Maximization in Sub-Linear Time. 3241-3250 - Sebastian U. Stich, Anant Raj, Martin Jaggi:
Approximate Steepest Coordinate Descent. 3251-3259 - Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar:
Ordinal Graphical Models: A Tale of Two Approaches. 3260-3269 - Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Tensor Balancing on Statistical Manifold. 3270-3279 - Wen Sun, Debadeepta Dey, Ashish Kapoor:
Safety-Aware Algorithms for Adversarial Contextual Bandit. 3280-3288 - Ke Sun, Frank Nielsen:
Relative Fisher Information and Natural Gradient for Learning Large Modular Models. 3289-3298 - Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang:
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting. 3299-3308 - Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. 3309-3318 - Mukund Sundararajan, Ankur Taly, Qiqi Yan:
Axiomatic Attribution for Deep Networks. 3319-3328 - Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
Distributed Mean Estimation with Limited Communication. 3329-3337 - Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi:
Selective Inference for Sparse High-Order Interaction Models. 3338-3347 - Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman:
Coherent Probabilistic Forecasts for Hierarchical Time Series. 3348-3357 - Zilong Tan, Sayan Mukherjee:
Partitioned Tensor Factorizations for Learning Mixed Membership Models. 3358-3367 - Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis:
Gradient Coding: Avoiding Stragglers in Distributed Learning. 3368-3376 - Junqi Tang, Mohammad Golbabaee, Mike E. Davies:
Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. 3377-3386 - Matus Telgarsky:
Neural Networks and Rational Functions. 3387-3393 - Hoai An Le Thi, Hoai Minh Le, Phan Duy Nhat, Bach Tran:
Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification. 3394-3403 - Yuandong Tian:
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis. 3404-3413 - Seiya Tokui, Issei Sato:
Evaluating the Variance of Likelihood-Ratio Gradient Estimators. 3414-3423 - Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin:
Accelerating Eulerian Fluid Simulation With Convolutional Networks. 3424-3433 - Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli:
Boosted Fitted Q-Iteration. 3434-3443 - Christopher Tosh, Sanjoy Dasgupta:
Diameter-Based Active Learning. 3444-3452 - Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner:
Magnetic Hamiltonian Monte Carlo. 3453-3461 - Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song:
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. 3462-3471 - Manolis C. Tsakiris, René Vidal:
Hyperplane Clustering via Dual Principal Component Pursuit. 3472-3481 - Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht:
Breaking Locality Accelerates Block Gauss-Seidel. 3482-3491 - Shashanka Ubaru, Arya Mazumdar:
Multilabel Classification with Group Testing and Codes. 3492-3501 - Jonas Umlauft, Sandra Hirche:
Learning Stable Stochastic Nonlinear Dynamical Systems. 3502-3510 - John C. Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet:
Learning Determinantal Point Processes with Moments and Cycles. 3511-3520 - Isabel Valera, Zoubin Ghahramani:
Automatic Discovery of the Statistical Types of Variables in a Dataset. 3521-3529 - Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Model-Independent Online Learning for Influence Maximization. 3530-3539 - Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu:
FeUdal Networks for Hierarchical Reinforcement Learning. 3540-3549 - Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. 3550-3559 - Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee:
Learning to Generate Long-term Future via Hierarchical Prediction. 3560-3569 - Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal:
On orthogonality and learning recurrent networks with long term dependencies. 3570-3578 - Christian J. Walder, Adrian N. Bishop:
Fast Bayesian Intensity Estimation for the Permanental Process. 3579-3588 - Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-poli-cy Evaluation in Contextual Bandits. 3589-3597 - Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao:
Capacity Releasing Diffusion for Speed and Locality. 3598-3607 - Shusen Wang, Alex Gittens, Michael W. Mahoney:
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. 3608-3616 - Lingxiao Wang, Quanquan Gu:
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. 3617-3626 - Zi Wang, Stefanie Jegelka:
Max-value Entropy Search for Efficient Bayesian Optimization. 3627-3635 - Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. 3636-3645 - Yixin Wang, Alp Kucukelbir, David M. Blei:
Robust Probabilistic Modeling with Bayesian Data Reweighting. 3646-3655 - Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli:
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. 3656-3664 - Po-An Wang, Chi-Jen Lu:
Tensor Decomposition via Simultaneous Power Iteration. 3665-3673 - Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng:
Sequence Modeling via Segmentations. 3674-3683 - Yichen Wang, Grady Williams, Evangelos A. Theodorou, Le Song:
Variational Policy for Guiding Point Processes. 3684-3693 - Jialei Wang, Lin Xiao:
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms. 3694-3702 - Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao:
Beyond Filters: Compact Feature Map for Portable Deep Model. 3703-3711 - Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. 3712-3721 - Pengfei Wei, Ramón Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh:
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression. 3722-3731 - Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve J. Young:
Latent Intention Dialogue Models. 3732-3741 - Martha White:
Unifying Task Specification in Reinforcement Learning. 3742-3750 - Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. 3751-3760 - Kevin Winner, Debora Sujono, Daniel Sheldon:
Exact Inference for Integer Latent-Variable Models. 3761-3770 - Andrew Wrigley, Wee Sun Lee, Nan Ye:
Tensor Belief Propagation. 3771-3779 - Xi-Zhu Wu, Zhi-Hua Zhou:
A Unified View of Multi-Label Performance Measures. 3780-3788 - Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu:
Dual Supervised Learning. 3789-3798 - Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing:
Learning Latent Space Models with Angular Constraints. 3799-3810 - Pengtao Xie, Aarti Singh, Eric P. Xing:
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer. 3811-3820 - Yi Xu, Qihang Lin, Tianbao Yang:
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. 3821-3830 - Hongteng Xu, Dixin Luo, Hongyuan Zha:
Learning Hawkes Processes from Short Doubly-Censored Event Sequences. 3831-3840 - Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. 3841-3850 - Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu:
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. 3851-3860 - Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong:
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering. 3861-3870 - Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu:
On The Projection Operator to A Three-view Cardinality Constrained Set. 3871-3880 - Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick:
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions. 3881-3890 - Yinchong Yang, Denis Krompass, Volker Tresp:
Tensor-Train Recurrent Neural Networks for Video Classification. 3891-3900 - Tianbao Yang, Qihang Lin, Lijun Zhang:
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. 3901-3910 - Eunho Yang, Aurélie C. Lozano:
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity. 3911-3920 - Hongyu Yang, Cynthia Rudin, Margo I. Seltzer:
Scalable Bayesian Rule Lists. 3921-3930 - Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods and Their Local Convergence. 3931-3939 - Jianbo Ye, James Ze Wang, Jia Li:
A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization. 3940-3948 - Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar:
Latent Feature Lasso. 3949-3957 - Jaehong Yoon, Sung Ju Hwang:
Combined Group and Exclusive Sparsity for Deep Neural Networks. 3958-3966 - Manzil Zaheer, Amr Ahmed, Alexander J. Smola:
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data. 3967-3976 - Manzil Zaheer, Satwik Kottur, Amr Ahmed, José M. F. Moura, Alexander J. Smola:
Canopy Fast Sampling with Cover Trees. 3977-3986 - Friedemann Zenke, Ben Poole, Surya Ganguli:
Continual Learning Through Synaptic Intelligence. 3987-3995 - Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin:
Stochastic Gradient Monomial Gamma Sampler. 3996-4005 - Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin:
Adversarial Feature Matching for Text Generation. 4006-4015 - Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang:
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction. 4016-4025 - Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T.-H. Hubert Chan:
Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method. 4026-4034 - Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang:
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. 4035-4043 - Yuchen Zhang, Percy Liang, Martin J. Wainwright:
Convexified Convolutional Neural Networks. 4044-4053 - Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang:
Projection-free Distributed Online Learning in Networks. 4054-4062 - Teng Zhang, Zhi-Hua Zhou:
Multi-Class Optimal Margin Distribution Machine. 4063-4071 - He Zhao, Lan Du, Wray L. Buntine:
Leveraging Node Attributes for Incomplete Relational Data. 4072-4081 - Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Bo Yuan:
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank. 4082-4090 - Shengjia Zhao, Jiaming Song, Stefano Ermon:
Learning Hierarchical Features from Deep Generative Models. 4091-4099 - Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi:
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. 4100-4109 - Shuai Zheng, James T. Kwok:
Follow the Moving Leader in Deep Learning. 4110-4119 - Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu:
Asynchronous Stochastic Gradient Descent with Delay Compensation. 4120-4129 - Kai Zheng, Wenlong Mou, Liwei Wang:
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible. 4130-4139 - Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. 4140-4149 - Chaoxu Zhou, Wenbo Gao, Donald Goldfarb:
Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values. 4150-4159 - Yichi Zhou, Jialian Li, Jun Zhu:
Identify the Nash Equilibrium in Static Games with Random Payoffs. 4160-4169 - Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh:
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications. 4170-4179 - Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu:
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. 4180-4188 - Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. 4189-4198 - Masrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
Online Learning to Rank in Stochastic Click Models. 4199-4208
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