default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID36th 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 - Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman:
Quantifying Generalization in Reinforcement Learning. 1282-1289 - Eldan Cohen, J. Christopher Beck:
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models. 1290-1299 - Alon Cohen, Tomer Koren, Yishay Mansour:
Learning Linear-Quadratic Regulators Efficiently with only √T Regret. 1300-1309 - Jeremy Cohen, Elan Rosenfeld, J. Zico Kolter:
Certified Adversarial Robustness via Randomized Smoothing. 1310-1320 - Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. 1321-1330 - Cédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani:
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning. 1331-1340 - Ronan Collobert, Awni Y. Hannun, Gabriel Synnaeve:
A fully differentiable beam search decoder. 1341-1350 - Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. 1351-1360 - Juan D. Correa, Jin Tian, Elias Bareinboim:
Adjustment Criteria for Generalizing Experimental Findings. 1361-1369 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang:
Online Learning with Sleeping Experts and Feedback Graphs. 1370-1378 - Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile:
Active Learning with Disagreement Graphs. 1379-1387 - Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Shape Constraints for Set Functions. 1388-1396 - Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. 1397-1405 - Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge blunts Bayes: Hardness Results for Adversarial Training. 1406-1415 - Zac Cranko, Richard Nock:
Boosted Density Estimation Remastered. 1416-1425 - Victoria G. Crawford, Alan Kuhnle, My T. Thai:
Submodular Cost Submodular Cover with an Approximate Oracle. 1426-1435 - Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. 1436-1445 - Ashok Cutkosky:
Anytime Online-to-Batch, Optimism and Acceleration. 1446-1454 - Ashok Cutkosky, Tamás Sarlós:
Matrix-Free Preconditioning in Online Learning. 1455-1464 - Milan Cvitkovic, Günther Koliander:
Minimal Achievable Sufficient Statistic Learning. 1465-1474 - Milan Cvitkovic, Badal Singh, Animashree Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. 1475-1485 - Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans:
The Value Function Polytope in Reinforcement Learning. 1486-1495 - Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
Bayesian Optimization Meets Bayesian Optimal Stopping. 1496-1506 - Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. 1507-1516 - Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré:
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. 1517-1527 - Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. 1528-1537 - Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau:
TarMAC: Targeted Multi-Agent Communication. 1538-1546 - Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu:
Teaching a black-box learner. 1547-1555 - Gwendoline de Bie, Gabriel Peyré, Marco Cuturi:
Stochastic Deep Networks. 1556-1565 - Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil:
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. 1566-1575 - Onur Dereli, Ceyda Oguz, Mehmet Gönen:
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology. 1576-1585 - Nichita Diaconu, Daniel E. Worrall:
Learning to Convolve: A Generalized Weight-Tying Approach. 1586-1595 - Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. 1596-1606 - Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan:
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization. 1607-1616 - Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal:
Noisy Dual Principal Component Pursuit. 1617-1625 - Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. 1626-1635 - Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel:
Trajectory-Based Off-Policy Deep Reinforcement Learning. 1636-1645 - Elvis Dohmatob:
Generalized No Free Lunch Theorem for Adversarial Robustness. 1646-1654 - Simon S. Du, Wei Hu:
Width Provably Matters in Optimization for Deep Linear Neural Networks. 1655-1664 - Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. 1665-1674 - Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai:
Gradient Descent Finds Global Minima of Deep Neural Networks. 1675-1685 - Junliang Du, Antonio R. Linero:
Incorporating Grouping Information into Bayesian Decision Tree Ensembles. 1686-1695 - Yilun Du, Karthik Narasimhan:
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning. 1696-1705 - Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning. 1706-1715 - Yonatan Dukler, Wuchen Li, Alex Tong Lin, Guido Montúfar:
Wasserstein of Wasserstein Loss for Learning Generative Models. 1716-1725 - Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani:
Learning interpretable continuous-time models of latent stochastic dynamical systems. 1726-1734 - Conor Durkan, Charlie Nash:
Autoregressive Energy Machines. 1735-1744 - Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin:
Band-limited Training and Inference for Convolutional Neural Networks. 1745-1754 - Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell Jr.:
Imitating Latent Policies from Observation. 1755-1763 - Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. 1764-1773 - Mohamed Elfeki, Camille Couprie, Morgane Rivière, Mohamed Elhoseiny:
GDPP: Learning Diverse Generations using Determinantal Point Processes. 1774-1783 - Ehsan Elhamifar:
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm. 1784-1793 - Alina Ene, Adrian Vladu:
Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares. 1794-1801 - Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry:
Exploring the Landscape of Spatial Robustness. 1802-1811 - Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia:
Cross-Domain 3D Equivariant Image Embeddings. 1812-1822 - Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. 1823-1832 - Matthew Fahrbach, Vahab S. Mirrokni, Morteza Zadimoghaddam:
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity. 1833-1842 - Yifeng Fan, Zhizhen Zhao:
Multi-Frequency Vector Diffusion Maps. 1843-1852 - Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm:
Stable-Predictive Optimistic Counterfactual Regret Minimization. 1853-1862 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Regret Circuits: Composability of Regret Minimizers. 1863-1872 - Mehdi Fatemi, Shikhar Sharma, Harm van Seijen, Samira Ebrahimi Kahou:
Dead-ends and Secure Exploration in Reinforcement Learning. 1873-1881 - Ilya Feige:
Invariant-Equivariant Representation Learning for Multi-Class Data. 1882-1891 - Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. 1892-1900 - Raphaël Féraud, Réda Alami, Romain Laroche:
Decentralized Exploration in Multi-Armed Bandits. 1901-1909 - Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher:
Almost surely constrained convex optimization. 1910-1919 - Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. 1920-1930 - Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin T. Vechev:
DL2: Training and Querying Neural Networks with Logic. 1931-1941 - Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. 1942-1951 - Edwin Fong, Simon Lyddon, Chris C. Holmes:
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. 1952-1962 - Vojtech Franc, Daniel Prusa:
On discriminative learning of prediction uncertainty. 1963-1971 - Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He:
Learning Discrete Structures for Graph Neural Networks. 1972-1982 - Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar:
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. 1983-1992 - Thomas Frerix, Joan Bruna:
Approximating Orthogonal Matrices with Effective Givens Factorization. 1993-2001 - Charlie Frogner, Tomaso A. Poggio:
Fast and Flexible Inference of Joint Distributions from their Marginals. 2002-2011 - Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. 2012-2020 - Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. 2021-2030 - Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin:
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement. 2031-2041 - Kaito Fujii, Shinsaku Sakaue:
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. 2042-2051 - Scott Fujimoto, David Meger, Doina Precup:
Off-Policy Deep Reinforcement Learning without Exploration. 2052-2062 - Shani Gamrian, Yoav Goldberg:
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation. 2063-2072 - Octavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn:
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. 2073-2082 - Hongyang Gao, Shuiwang Ji:
Graph U-Nets. 2083-2092 - Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang:
Deep Generative Learning via Variational Gradient Flow. 2093-2101 - Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh:
Rate Distortion For Model Compression: From Theory To Practice. 2102-2111 - Hongchang Gao, Jian Pei, Heng Huang:
Demystifying Dropout. 2112-2121 - Feng Gao, Guy Wolf, Matthew J. Hirn:
Geometric Scattering for Graph Data Analysis. 2122-2131 - Tingran Gao, Zhizhen Zhao:
Multi-Frequency Phase Synchronization. 2132-2141 - Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal Mini-Batch and Step Sizes for SAGA. 2142-2150 - Yonatan Geifman, Ran El-Yaniv:
SelectiveNet: A Deep Neural Network with an Integrated Reject Option. 2151-2159 - Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
A Theory of Regularized Markov Decision Processes. 2160-2169 - Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. 2170-2179 - Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo:
Partially Linear Additive Gaussian Graphical Models. 2180-2190 - Hossein Shokri Ghadikolaei, Hadi G. Ghauch, Carlo Fischione, Mikael Skoglund:
Learning and Data Selection in Big Datasets. 2191-2200 - Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrovic:
Improved Parallel Algorithms for Density-Based Network Clustering. 2201-2210 - Badih Ghazi, Rina Panigrahy, Joshua R. Wang:
Recursive Sketches for Modular Deep Learning. 2211-2220 - Behrooz Ghorbani, Hamid Javadi, Andrea Montanari:
An Instability in Variational Inference for Topic Models. 2221-2231 - Behrooz Ghorbani, Shankar Krishnan, Ying Xiao:
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density. 2232-2241 - Amirata Ghorbani, James Y. Zou:
Data Shapley: Equitable Valuation of Data for Machine Learning. 2242-2251 - Dar Gilboa, Sam Buchanan, John Wright:
Efficient Dictionary Learning with Gradient Descent. 2252-2259 - Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii:
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. 2260-2268 - Jon Gillick, Adam Roberts, Jesse H. Engel, Douglas Eck, David Bamman:
Learning to Groove with Inverse Sequence Transformations. 2269-2279 - Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. 2280-2289 - Jaime Roquero Gimenez, James Y. Zou:
Discovering Conditionally Salient Features with Statistical Guarantees. 2290-2298 - Ziv Goldfeld, Ewout van den Berg, Kristjan H. Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy:
Estimating Information Flow in Deep Neural Networks. 2299-2308 - Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. 2309-2318 - Sreenivas Gollapudi, Debmalya Panigrahi:
Online Algorithms for Rent-Or-Buy with Expert Advice. 2319-2327 - Alexander Golovnev, Dávid Pál, Balázs Szörényi:
The information-theoretic value of unlabeled data in semi-supervised learning. 2328-2336 - Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu:
Efficient Training of BERT by Progressively Stacking. 2337-2346 - ChengYue Gong, Jian Peng, Qiang Liu:
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization. 2347-2356 - Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes:
Obtaining Fairness using Optimal Transport Theory. 2357-2365 - Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining parametric and nonparametric models for off-policy evaluation. 2366-2375 - Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee:
Counterfactual Visual Explanations. 2376-2384 - James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote:
Adaptive Sensor Placement for Continuous Spaces. 2385-2393 - Alexander Greaves-Tunnell, Zaïd Harchaoui:
A Statistical Investigation of Long Memory in Language and Music. 2394-2403 - David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke:
Automatic Posterior Transformation for Likelihood-Free Inference. 2404-2414 - Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel:
Learning to Optimize Multigrid PDE Solvers. 2415-2423 - Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. 2424-2433 - Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. 2434-2444 - Jiaqi Gu, Guosheng Yin:
Fast Algorithm for Generalized Multinomial Models with Ranking Data. 2445-2453 - Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie:
Towards a Deep and Unified Understanding of Deep Neural Models in NLP. 2454-2463 - Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap:
An Investigation of Model-Free Planning. 2464-2473 - Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai:
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops. 2474-2483 - Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. 2484-2493 - Tian Guo, Tao Lin, Nino Antulov-Fantulin:
Exploring interpretable LSTM neural networks over multi-variable data. 2494-2504 - Lingbing Guo, Zequn Sun, Wei Hu:
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. 2505-2514 - Albert Gural, Boris Murmann:
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. 2515-2524 - Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet A Forward Stable Deep Neural Network. 2525-2534 - Guy Hacohen, Daphna Weinshall:
On The Power of Curriculum Learning in Training Deep Networks. 2535-2544 - Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe:
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization. 2545-2554 - Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. 2555-2565 - Tavi Halperin, Ariel Ephrat, Yedid Hoshen:
Neural Separation of Observed and Unobserved Distributions. 2566-2575 - Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang:
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. 2576-2585 - Seungyul Han, Youngchul Sung:
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning. 2586-2595 - Boris Hanin, David Rolnick:
Complexity of Linear Regions in Deep Networks. 2596-2604 - Josiah Hanna, Scott Niekum, Peter Stone:
Importance Sampling Policy Evaluation with an Estimated Behavior Policy. 2605-2613 - Yi Hao, Alon Orlitsky:
Doubly-Competitive Distribution Estimation. 2614-2623 - Jeff Z. HaoChen, Suvrit Sra:
Random Shuffling Beats SGD after Finite Epochs. 2624-2633 - Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi:
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications. 2634-2643 - Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. 2644-2652 - Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu:
Submodular Observation Selection and Information Gathering for Quadratic Models. 2653-2662 - Doron Haviv, Alexander Rivkind, Omri Barak:
Understanding and Controlling Memory in Recurrent Neural Networks. 2663-2671 - Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation function on Deep Neural Networks Training. 2672-2680 - Elad Hazan, Sham M. Kakade, Karan Singh, Abby Van Soest:
Provably Efficient Maximum Entropy Exploration. 2681-2691 - Hoda Heidari, Vedant Nanda, Krishna P. Gummadi:
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning. 2692-2701 - Julien M. Hendrickx, Alexander Olshevsky, Venkatesh Saligrama:
Graph Resistance and Learning from Pairwise Comparisons. 2702-2711 - Dan Hendrycks, Kimin Lee, Mantas Mazeika:
Using Pre-Training Can Improve Model Robustness and Uncertainty. 2712-2721 - Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design. 2722-2730 - Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel:
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. 2731-2741 - Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford:
Collective Model Fusion for Multiple Black-Box Experts. 2742-2750 - Christoph D. Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit:
Connectivity-Optimized Representation Learning via Persistent Homology. 2751-2760 - Matthew J. Holland, Kazushi Ikeda:
Better generalization with less data using robust gradient descent. 2761-2770 - Emiel Hoogeboom, Rianne van den Berg, Max Welling:
Emerging Convolutions for Generative Normalizing Flows. 2771-2780 - Samuel Horváth, Peter Richtárik:
Nonconvex Variance Reduced Optimization with Arbitrary Sampling. 2781-2789 - Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly:
Parameter-Efficient Transfer Learning for NLP. 2790-2799 - Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar:
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging. 2800-2809 - Ya-Ping Hsieh, Chen Liu, Volkan Cevher:
Finding Mixed Nash Equilibria of Generative Adversarial Networks. 2810-2819 - Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. 2820-2829 - Kelvin Hsu, Fabio Ramos:
Bayesian Deconditional Kernel Mean Embeddings. 2830-2838 - Feihu Huang, Songcan Chen, Heng Huang:
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization. 2839-2848 - Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu:
Unsupervised Deep Learning by Neighbourhood Discovery. 2849-2858 - Kejun Huang, Xiao Fu:
Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm. 2859-2868 - Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron C. Courville:
Hierarchical Importance Weighted Autoencoders. 2869-2878 - Lingxiao Huang, Nisheeth K. Vishnoi:
Stable and Fair Classification. 2879-2890 - Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Ángel Bautista, Shih-Yu Sun, Carlos Guestrin, Joshua M. Susskind:
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment. 2891-2900 - Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. 2901-2910 - Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Composing Entropic Policies using Divergence Correction. 2911-2920 - Uiwon Hwang, Dahuin Jung, Sungroh Yoon:
HexaGAN: Generative Adversarial Nets for Real World Classification. 2921-2930 - Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. 2931-2940 - Craig Innes, Alex Lascarides:
Learning Structured Decision Problems with Unawareness. 2941-2950 - Niels Bruun Ipsen, Lars Kai Hansen:
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! 2951-2960 - Shariq Iqbal, Fei Sha:
Actor-Attention-Critic for Multi-Agent Reinforcement Learning. 2961-2970 - Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. 2971-2980 - Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence: Completeness Results. 2981-2989 - Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin:
Learning from a Learner. 2990-2999 - Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. 3000-3008 - Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. 3009-3018 - Jennifer Jang, Heinrich Jiang:
DBSCAN++: Towards fast and scalable density clustering. 3019-3029 - Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin:
Learning What and Where to Transfer. 3030-3039 - Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas:
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. 3040-3049 - Nathan Jay, Noga H. Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar:
A Deep Reinforcement Learning Perspective on Internet Congestion Control. 3050-3059 - Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam:
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. 3060-3070 - Taewon Jeong, Youngmin Lee, Heeyoung Kim:
Ladder Capsule Network. 3071-3079 - Jongheon Jeong, Jinwoo Shin:
Training CNNs with Selective Allocation of Channels. 3080-3090 - Yeonwoo Jeong, Hyun Oh Song:
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement. 3091-3099 - Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. 3100-3109 - Zhengyao Jiang, Shan Luo:
Neural Logic Reinforcement Learning. 3110-3119 - Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. 3120-3129 - Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris:
Discovering Options for Exploration by Minimizing Cover Time. 3130-3139 - Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. 3140-3151 - David John, Vincent Heuveline, Michael Schober:
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. 3152-3162 - Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak:
Bilinear Bandits with Low-rank Structure. 3163-3172 - Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel D. Procaccia, Christos-Alexandros Psomas:
Statistical Foundations of Virtual Democracy. 3173-3182 - Hiroshi Kajino:
Molecular Hypergraph Grammar with Its Application to Molecular Optimization. 3183-3191 - Dimitris Kalimeris, Gal Kaplun, Yaron Singer:
Robust Influence Maximization for Hyperparametric Models. 3192-3200 - Nathan Kallus:
Classifying Treatment Responders Under Causal Effect Monotonicity. 3201-3210 - Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra:
Trainable Decoding of Sets of Sequences for Neural Sequence Models. 3211-3221 - Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. 3222-3232 - Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Differentially Private Learning of Geometric Concepts. 3233-3241 - Christos Kaplanis, Murray Shanahan, Claudia Clopath:
Policy Consolidation for Continual Reinforcement Learning. 3242-3251 - Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. 3252-3261 - Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra:
Riemannian adaptive stochastic gradient algorithms on matrix manifolds. 3262-3271 - Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik:
Neural Inverse Knitting: From Images to Manufacturing Instructions. 3272-3281 - Angelos Katharopoulos, François Fleuret:
Processing Megapixel Images with Deep Attention-Sampling Models. 3282-3291 - Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis:
Robust Estimation of Tree Structured Gaussian Graphical Models. 3292-3300 - Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras:
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking. 3301-3310 - Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi:
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity. 3311-3320 - Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth:
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. 3321-3330 - Tom Kenter, Vincent Wan, Chun-an Chan, Rob Clark, Jakub Vit:
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network. 3331-3340 - Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. 3341-3350 - Renata Khasanova, Pascal Frossard:
Geometry Aware Convolutional Filters for Omnidirectional Images Representation. 3351-3359 - Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song:
EMI: Exploration with Mutual Information. 3360-3369 - Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon:
FloWaveNet : A Generative Flow for Raw Audio. 3370-3378 - Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim:
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty. 3379-3388 - Gi-Soo Kim, Myunghee Cho Paik:
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model. 3389-3397 - Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman:
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension. 3398-3407 - Friso H. Kingma, Pieter Abbeel, Jonathan Ho:
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. 3408-3417 - Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
CompILE: Compositional Imitation Learning and Execution. 3418-3428 - Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause:
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. 3429-3438 - Ross Kleiman, David Page:
AUCμ: A Performance Metric for Multi-Class Machine Learning Models. 3439-3447 - Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. 3448-3457 - Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. 3458-3467 - Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin:
POPQORN: Quantifying Robustness of Recurrent Neural Networks. 3468-3477 - Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. 3478-3487 - Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. 3488-3498 - Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. 3499-3508 - Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. 3509-3518 - Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. 3519-3529 - Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Cesar A. Uribe:
On the Complexity of Approximating Wasserstein Barycenters. 3530-3540 - Andrei Kulunchakov, Julien Mairal:
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization. 3541-3550 - Ravi Kumar, Rina Panigrahy, Ali Rahimi, David P. Woodruff:
Faster Algorithms for Binary Matrix Factorization. 3551-3559 - Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed:
Loss Landscapes of Regularized Linear Autoencoders. 3560-3569 - Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright:
Geometry and Symmetry in Short-and-Sparse Deconvolution. 3570-3580 - Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly:
A Large-Scale Study on Regularization and Normalization in GANs. 3581-3590 - Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Making Decisions that Reduce Discriminatory Impacts. 3591-3600 - Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. 3601-3610 - Antoine Labatie:
Characterizing Well-Behaved vs. Pathological Deep Neural Networks. 3611-3621 - Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. 3622-3631 - Sylvain Lamprier:
A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion. 3632-3641 - Joong-Ho Won, Jason Xu, Kenneth Lange:
Projection onto Minkowski Sums with Application to Constrained Learning. 3642-3651 - Romain Laroche, Paul Trichelair, Remi Tachet des Combes:
Safe Policy Improvement with Baseline Bootstrapping. 3652-3661 - Silvio Lattanzi, Christian Sohler:
A Better k-means++ Algorithm via Local Search. 3662-3671 - Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel:
Lorentzian Distance Learning for Hyperbolic Representations. 3672-3681 - Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell:
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. 3682-3691 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvári, Gellért Weisz:
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction. 3692-3702 - Hoang Minh Le, Cameron Voloshin, Yisong Yue:
Batch Policy Learning under Constraints. 3703-3712 - Donghwan Lee, Niao He:
Target-Based Temporal-Difference Learning. 3713-3722 - Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. 3723-3733 - Junhyun Lee, Inyeop Lee, Jaewoo Kang:
Self-Attention Graph Pooling. 3734-3743 - Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. 3744-3753 - Ching-pei Lee, Stephen J. Wright:
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems. 3754-3762 - Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin:
Robust Inference via Generative Classifiers for Handling Noisy Labels. 3763-3772 - Yifan Lei, Qiang Huang, Mohan S. Kankanhalli, Anthony K. H. Tung:
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space. 3773-3781 - Matthieu Lerasle, Zoltán Szabó, Timothée Mathieu, Guillaume Lecué:
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means. 3782-3793 - Mario Lezcano Casado, David Martínez-Rubio:
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group. 3794-3803 - Yingzhen Li, John Bradshaw, Yash Sharma:
Are Generative Classifiers More Robust to Adversarial Attacks? 3804-3814 - Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear quantum algorithms for training linear and kernel-based classifiers. 3815-3824 - Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu:
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. 3825-3834 - Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli:
Graph Matching Networks for Learning the Similarity of Graph Structured Objects. 3835-3845 - Yang Li, Lukasz Kaiser, Samy Bengio, Si Si:
Area Attention. 3846-3855 - Shuai Li, Tor Lattimore, Csaba Szepesvári:
Online Learning to Rank with Features. 3856-3865 - Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong:
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. 3866-3876 - Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark:
Bayesian Joint Spike-and-Slab Graphical Lasso. 3877-3885 - Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn:
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. 3886-3895 - Juncheng Li, Frank R. Schmidt, J. Zico Kolter:
Adversarial camera stickers: A physical camera-based attack on deep learning systems. 3896-3904