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34th 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