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36th ICML 2019: Long Beach, California, USA
- Kamalika Chaudhuri, Ruslan Salakhutdinov:
Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research 97, PMLR 2019 - Gabriele Abbati, Philippe Wenk, Michael A. Osborne
, Andreas Krause, Bernhard Schölkopf, Stefan Bauer:
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. 1-10 - Axel Abels
, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher:
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. 11-20 - Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan:
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. 21-29 - Jayadev Acharya, Clément L. Canonne
, Himanshu Tyagi:
Communication-Constrained Inference and the Role of Shared Randomness. 30-39 - Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. 40-50 - Jayadev Acharya, Ziteng Sun:
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters. 51-60 - Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria:
Learning Models from Data with Measurement Error: Tackling Underreporting. 61-70 - Tameem Adel, Adrian Weller:
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning. 71-81 - Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Anil Vullikanti:
PAC Learnability of Node Functions in Networked Dynamical Systems. 82-91 - Ashish Agarwal:
Static Automatic Batching In TensorFlow. 92-101 - Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
Efficient Full-Matrix Adaptive Regularization. 102-110 - Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. 111-119 - Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. 120-129 - Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. 130-140 - Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. 141-150 - Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the Impact of Entropy on Policy Optimization. 151-160 - Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp:
Fairwashing: the risk of rationalization. 161-170 - Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito
, Kouhei Nishida:
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. 171-180 - Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann:
Projections for Approximate Policy Iteration Algorithms. 181-190 - Ahmed M. Alaa, Mihaela van der Schaar:
Validating Causal Inference Models via Influence Functions. 191-201 - Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas:
Multi-objective training of Generative Adversarial Networks with multiple discriminators. 202-211 - Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauzá Villalonga, Alberto Rodriguez, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Graph Element Networks: adaptive, structured computation and memory. 212-222 - Carl Allen, Timothy M. Hospedales:
Analogies Explained: Towards Understanding Word Embeddings. 223-231 - Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum:
Infinite Mixture Prototypes for Few-shot Learning. 232-241 - Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
A Convergence Theory for Deep Learning via Over-Parameterization. 242-252 - Ahsan S. Alvi, Bin Xin Ru, Jan-Peter Calliess, Stephen J. Roberts, Michael A. Osborne
:
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. 253-262 - Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. 263-271 - Marco Ancona, Cengiz Öztireli, Markus H. Gross:
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation. 272-281 - Jesse Anderton, Javed A. Aslam:
Scaling Up Ordinal Embedding: A Landmark Approach. 282-290 - Cem Anil, James Lucas, Roger B. Grosse:
Sorting Out Lipschitz Function Approximation. 291-301 - Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi:
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. 302-311 - Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness:
Unsupervised Label Noise Modeling and Loss Correction. 312-321 - Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li
, Ruosong Wang:
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. 322-332 - Sepehr Assadi, MohammadHossein Bateni, Vahab S. Mirrokni:
Distributed Weighted Matching via Randomized Composable Coresets. 333-343 - Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. 344-353 - Raul Astudillo, Peter I. Frazier:
Bayesian Optimization of Composite Functions. 354-363 - Kubilay Atasu, Thomas Mittelholzer:
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations. 364-373 - Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. 374-384 - Sergül Aydöre, Bertrand Thirion, Gaël Varoquaux:
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data. 385-394 - Fadhel Ayed, Juho Lee, Francois Caron:
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior. 395-404 - Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian
, Tal Wagner:
Scalable Fair Clustering. 405-413 - Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. 414-423 - Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. 424-433 - David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel:
Open-ended learning in symmetric zero-sum games. 434-443 - Muhammed Fatih Balin, Abubakar Abid, James Y. Zou:
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction. 444-453 - Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox:
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving. 454-463 - Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. 464-474 - Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko:
Learning to Route in Similarity Graphs. 475-484 - Pablo V. A. Barros, German Ignacio Parisi, Stefan Wermter:
A Personalized Affective Memory Model for Improving Emotion Recognition. 485-494 - Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko:
Scale-free adaptive planning for deterministic dynamics & discounted rewards. 495-504 - Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai:
Pareto Optimal Streaming Unsupervised Classification. 505-514 - MohammadHossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh:
Categorical Feature Compression via Submodular Optimization. 515-523 - Joshua Batson, Loïc Royer:
Noise2Self: Blind Denoising by Self-Supervision. 524-533 - Alex Beatson, Ryan P. Adams:
Efficient optimization of loops and limits with randomized telescoping sums. 534-543 - Philipp Becker, Harit Pandya, Gregor H. W. Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann:
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces. 544-552 - Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt
:
Switching Linear Dynamics for Variational Bayes Filtering. 553-562 - Sima Behpour, Anqi Liu, Brian D. Ziebart:
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. 563-572 - Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen:
Invertible Residual Networks. 573-582 - Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale To ImageNet. 583-593 - Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat:
Overcoming Multi-model Forgetting. 594-603 - Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger:
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning. 604-613 - Martín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, Guillermo Sapiro:
Adversarially Learned Representations for Information Obfuscation and Inference. 614-623 - Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang:
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. 624-633 - Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo:
Analyzing Federated Learning through an Adversarial Lens. 634-643 - Yatao An Bian, Joachim M. Buhmann, Andreas Krause
:
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference. 644-653 - Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. 654-663 - Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal:
A Kernel Perspective for Regularizing Deep Neural Networks. 664-674 - Yochai Blau, Tomer Michaeli:
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. 675-685 - Vinay Praneeth Boda, Prashanth L. A.:
Correlated bandits or: How to minimize mean-squared error online. 686-694 - Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings via Graph Poisoning. 695-704 - Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause:
Online Variance Reduction with Mixtures. 705-714 - Avishek Joey Bose, William L. Hamilton:
Compositional Fairness Constraints for Graph Embeddings. 715-724 - Xavier Bouthillier, César Laurent, Pascal Vincent:
Unreproducible Research is Reproducible. 725-734 - Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen J. Wright:
Blended Conditonal Gradients. 735-743 - Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Ordered Weighted Clustering. 744-753 - Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
Target Tracking for Contextual Bandits: Application to Demand Side Management. 754-763 - Robert A. Bridges, Anthony D. Gruber, Christopher Felder
, Miki E. Verma, Chelsey Hoff:
Active Manifolds: A non-linear analogue to Active Subspaces. 764-772 - David H. Brookes, Hahnbeom Park, Jennifer Listgarten:
Conditioning by adaptive sampling for robust design. 773-782 - Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. 783-792 - Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm:
Deep Counterfactual Regret Minimization. 793-802 - Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel:
Understanding the Origins of Bias in Word Embeddings. 803-811 - Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha:
Low Latency Privacy Preserving Inference. 812-821 - Alon Brutzkus, Amir Globerson:
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. 822-830 - Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn:
Adversarial examples from computational constraints. 831-840 - Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias:
Self-similar Epochs: Value in arrangement. 841-850 - Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. 851-861 - David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. 862-871 - Jonathon Byrd, Zachary Chase Lipton:
What is the Effect of Importance Weighting in Deep Learning? 872-881 - Yongqiang Cai, Qianxiao Li, Zuowei Shen:
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. 882-890 - Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. 891-901 - Gregory Canal, Andrew K. Massimino, Mark A. Davenport, Christopher J. Rozell:
Active Embedding Search via Noisy Paired Comparisons. 902-911 - Junyu Cao, Wei Sun:
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem. 912-920 - Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu:
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. 921-930 - Henry Chai, Jean-Francois Ton, Michael A. Osborne
, Roman Garnett:
Automated Model Selection with Bayesian Quadrature. 931-940 - Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas:
Learning Action Representations for Reinforcement Learning. 941-950 - Chun-Hao Chang, Mingjie Mai, Anna Goldenberg:
Dynamic Measurement Scheduling for Event Forecasting using Deep RL. 951-960 - Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
On Symmetric Losses for Learning from Corrupted Labels. 961-970 - Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett:
Online learning with kernel losses. 971-980 - Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian:
Neural Network Attributions: A Causal Perspective. 981-990 - Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits. 991-1000 - George H. Chen:
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates. 1001-1010 - Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates:
Stein Point Markov Chain Monte Carlo. 1011-1021 - Xinshi Chen, Hanjun Dai, Le Song:
Particle Flow Bayes' Rule. 1022-1031 - Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala:
Proportionally Fair Clustering. 1032-1041 - Jinglin Chen, Nan Jiang:
Information-Theoretic Considerations in Batch Reinforcement Learning. 1042-1051 - Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song:
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. 1052-1061 - Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang:
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. 1062-1070 - Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng:
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. 1071-1080 - Xinyang Chen, Sinan Wang, Mingsheng Long
, Jianmin Wang
:
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. 1081-1090 - Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse:
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications. 1091-1101 - Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang:
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. 1102-1111 - Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin:
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching. 1112-1121 - Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh:
Robust Decision Trees Against Adversarial Examples. 1122-1131 - Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang:
RaFM: Rank-Aware Factorization Machines. 1132-1140 - Richard Cheng, Abhinav Verma
, Gábor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick:
Control Regularization for Reduced Variance Reinforcement Learning. 1141-1150 - Ching-An Cheng, Xinyan Yan, Nathan D. Ratliff, Byron Boots:
Predictor-Corrector Policy Optimization. 1151-1161 - Julien Chiquet, Stéphane Robin, Mahendra Mariadassou:
Variational Inference for sparse network reconstruction from count data. 1162-1171 - Uthsav Chitra, Benjamin J. Raphael:
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights. 1172-1181 - Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon:
Neural Joint Source-Channel Coding. 1182-1192 - Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf:
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. 1193-1202 - Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller:
Unifying Orthogonal Monte Carlo Methods. 1203-1212 - Casey Chu, Jose H. Blanchet, Peter W. Glynn:
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. 1213-1222 - Eric Chu, Peter J. Liu:
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. 1223-1232 - Hye Won Chung, Ji Oon Lee:
Weak Detection of Signal in the Spiked Wigner Model. 1233-1241 - Ferdinando Cicalese, Eduardo Sany Laber, Lucas Murtinho:
New results on information theoretic clustering. 1242-1251 - Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim:
Sensitivity Analysis of Linear Structural Causal Models. 1252-1261 - Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff:
Dimensionality Reduction for Tukey Regression. 1262-1271 - Stéphan Clémençon, Pierre Laforgue, Patrice Bertail:
On Medians of (Randomized) Pairwise Means. 1272-1281