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ORCID37th ICML 2020: Virtual Event
- Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event. Proceedings of Machine Learning Research 119, PMLR 2020
- Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White:
Selective Dyna-Style Planning Under Limited Model Capacity. 1-10 - Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller:
A distributional view on multi-objective policy optimization. 11-22 - Marc Abeille, Alessandro Lazaric:
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. 23-31 - Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. 32-41 - Vinayak Abrol, Pulkit Sharma:
A Geometric Approach to Archetypal Analysis via Sparse Projections. 42-51 - Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context Aware Local Differential Privacy. 52-62 - Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco:
Efficient Intervention Design for Causal Discovery with Latents. 63-73 - Ben Adlam, Jeffrey Pennington:
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. 74-84 - Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil:
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions. 85-95 - Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu:
Boosting for Control of Dynamical Systems. 96-103 - Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi:
An Optimistic Perspective on Offline Reinforcement Learning. 104-114 - Rohit Agrawal, Thibaut Horel:
Optimal Bounds between f-Divergences and Integral Probability Metrics. 115-124 - Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash:
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments. 125-133 - Sungsoo Ahn, Younggyo Seo, Jinwoo Shin:
Learning What to Defer for Maximum Independent Sets. 134-144 - Kartik Ahuja, Karthikeyan Shanmugam
, Kush R. Varshney, Amit Dhurandhar:
Invariant Risk Minimization Games. 145-155 - Laurence Aitchison:
Why bigger is not always better: on finite and infinite neural networks. 156-164 - Ahmed M. Alaa, Mihaela van der Schaar:
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions. 165-174 - Ahmed M. Alaa, Mihaela van der Schaar:
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. 175-190 - Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher:
Random extrapolation for primal-dual coordinate descent. 191-201 - Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher:
A new regret analysis for Adam-type algorithms. 202-210 - Réda Alami, Odalric Maillard, Raphaël Féraud:
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay. 211-221 - Amr Alexandari, Anshul Kundaje, Avanti Shrikumar:
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation. 222-232 - Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani:
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. 233-244 - Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav:
Structural Language Models of Code. 245-256 - Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann:
LowFER: Low-rank Bilinear Pooling for Link Prediction. 257-268 - Ron Amit, Ron Meir, Kamil Ciosek:
Discount Factor as a Regularizer in Reinforcement Learning. 269-278 - Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida:
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning". 279-290 - Brandon Amos, Denis Yarats:
The Differentiable Cross-Entropy Method. 291-302 - Keerti Anand, Rong Ge, Debmalya Panigrahi:
Customizing ML Predictions for Online Algorithms. 303-313 - Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel:
Fairwashing explanations with off-manifold detergent. 314-323 - Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley:
Population-Based Black-Box Optimization for Biological Sequence Design. 324-334 - Ivan Anokhin, Dmitry Yarotsky:
Low-loss connection of weight vectors: distribution-based approaches. 335-344 - Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon:
Online metric algorithms with untrusted predictions. 345-355 - Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian:
NADS: Neural Architecture Distribution Search for Uncertainty Awareness. 356-366 - Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi:
Provable Representation Learning for Imitation Learning via Bi-level Optimization. 367-376 - Srinivasan Arunachalam, Reevu Maity:
Quantum Boosting. 377-387 - Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Black-box Certification and Learning under Adversarial Perturbations. 388-398 - Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, Paul Hand:
Invertible generative models for inverse problems: mitigating representation error and dataset bias. 399-409 - Mahmoud Assran, Mike Rabbat:
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. 410-420 - Alper Atamtürk, Andrés Gómez:
Safe screening rules for L0-regression from Perspective Relaxations. 421-430 - Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. 431-441 - Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant:
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. 442-451 - Kyriakos Axiotis, Maxim Sviridenko:
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding. 452-462 - Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang:
Model-Based Reinforcement Learning with Value-Targeted Regression. 463-474 - Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney:
Forecasting Sequential Data Using Consistent Koopman Autoencoders. 475-485 - Gregor Bachmann, Gary Bécigneul, Octavian Ganea:
Constant Curvature Graph Convolutional Networks. 486-496 - Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya P. Razenshteyn, Tal Wagner:
Scalable Nearest Neighbor Search for Optimal Transport. 497-506 - Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell:
Agent57: Outperforming the Atari Human Benchmark. 507-517 - Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz:
Fiduciary Bandits. 518-527 - Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh:
Learning De-biased Representations with Biased Representations. 528-539 - Dara Bahri, Heinrich Jiang, Maya R. Gupta:
Deep k-NN for Noisy Labels. 540-550 - Yu Bai, Chi Jin:
Provable Self-Play Algorithms for Competitive Reinforcement Learning. 551-560 - Liang Bai, Jiye Liang:
Sparse Subspace Clustering with Entropy-Norm. 561-568 - Daniel N. Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering in Graphs of Bounded Treewidth. 569-579 - Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Refined bounds for algorithm configuration: The knife-edge of dual class approximability. 580-590 - Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts:
Ready Policy One: World Building Through Active Learning. 591-601 - Marin Ballu, Quentin Berthet, Francis R. Bach:
Stochastic Optimization for Regularized Wasserstein Estimators. 602-612 - Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Dual Mirror Descent for Online Allocation Problems. 613-628 - Subho S. Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar K. Iyer:
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters. 629-641 - Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon:
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training. 642-652 - Runxue Bao, Bin Gu, Heng Huang:
Fast OSCAR and OWL Regression via Safe Screening Rules. 653-663 - Amitay Bar, Ronen Talmon, Ron Meir:
Option Discovery in the Absence of Rewards with Manifold Analysis. 664-674 - Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis:
Learning the piece-wise constant graph structure of a varying Ising model. 675-684 - Ronen Basri, Meirav Galun, Amnon Geifman, David W. Jacobs, Yoni Kasten, Shira Kritchman:
Frequency Bias in Neural Networks for Input of Non-Uniform Density. 685-694 - Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. 695-703 - Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan:
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications. 704-714 - Samyadeep Basu, Xuchen You, Soheil Feizi:
On Second-Order Group Influence Functions for Black-Box Predictions. 715-724 - Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel interpolation with continuous volume sampling. 725-735 - Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs. 736-745 - Pierre Bellec, Dana Yang:
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers. 746-755 - Christopher M. Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva:
Defense Through Diverse Directions. 756-766 - Emmanuel Bengio, Joelle Pineau, Doina Precup:
Interference and Generalization in Temporal Difference Learning. 767-777 - Viktor Bengs, Eyke Hüllermeier:
Preselection Bandits. 778-787 - Andrew Bennett, Nathan Kallus:
Efficient Policy Learning from Surrogate-Loss Classification Reductions. 788-798 - Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Training Neural Networks for and by Interpolation. 799-809 - Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. 810-821 - Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Learning with Imperfect Hints. 822-831 - Robi Bhattacharjee, Kamalika Chaudhuri:
When are Non-Parametric Methods Robust? 832-841 - Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, N. Variyam Vinodchandran:
Learning and Sampling of Atomic Interventions from Observations. 842-853 - Chiranjib Bhattacharyya, Ravindran Kannan:
Near-optimal sample complexity bounds for learning Latent k-polytopes and applications to Ad-Mixtures. 854-863 - Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. 864-873 - Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi:
Spectral Clustering with Graph Neural Networks for Graph Pooling. 874-883 - Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar:
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. 884-895 - Pavol Bielik, Martin T. Vechev:
Adversarial Robustness for Code. 896-907 - Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts:
The Boomerang Sampler. 908-918 - Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy:
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. 919-929 - Ilai Bistritz, Tavor Z. Baharav, Amir Leshem, Nicholas Bambos:
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits. 930-940 - Guy Blanc, Jane Lange, Li-Yang Tan:
Provable guarantees for decision tree induction: the agnostic setting. 941-949 - Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga:
Fast Differentiable Sorting and Ranking. 950-959 - Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? 960-969 - Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek:
Modulating Surrogates for Bayesian Optimization. 970-979 - Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson:
Deep Coordination Graphs. 980-991 - Alexander Bogatskiy, Brandon M. Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor:
Lorentz Group Equivariant Neural Network for Particle Physics. 992-1002 - Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. 1003-1013 - Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel:
Proper Network Interpretability Helps Adversarial Robustness in Classification. 1014-1023 - Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan:
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. 1024-1034 - Jörg Bornschein, Francesco Visin, Simon Osindero:
Small Data, Big Decisions: Model Selection in the Small-Data Regime. 1035-1044 - Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton:
Latent Variable Modelling with Hyperbolic Normalizing Flows. 1045-1055 - Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi:
Tightening Exploration in Upper Confidence Reinforcement Learning. 1056-1066 - Amanda Bower, Laura Balzano:
Preference Modeling with Context-Dependent Salient Features. 1067-1077 - Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi:
Adversarial Filters of Dataset Biases. 1078-1088 - Mark Braverman, Xinyi Chen, Sham M. Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang:
Calibration, Entropy Rates, and Memory in Language Models. 1089-1099 - Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff:
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. 1100-1110 - Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan:
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. 1111-1122 - Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson:
Estimating the Number and Effect Sizes of Non-null Hypotheses. 1123-1133 - Adam Breuer, Eric Balkanski, Yaron Singer:
The FAST Algorithm for Submodular Maximization. 1134-1143 - Marc Brockschmidt:
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. 1144-1152 - John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner:
TaskNorm: Rethinking Batch Normalization for Meta-Learning. 1153-1164 - Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum:
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. 1165-1177 - Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. 1178-1189 - Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner:
Scalable Exact Inference in Multi-Output Gaussian Processes. 1190-1201 - Jinzhi Bu, David Simchi-Levi, Yunzong Xu:
Online Pricing with Offline Data: Phase Transition and Inverse Square Law. 1202-1210 - Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag:
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. 1211-1219 - Maarten Buyl, Tijl De Bie:
DeBayes: a Bayesian Method for Debiasing Network Embeddings. 1220-1229 - Vivien Cabannes, Alessandro Rudi, Francis R. Bach:
Structured Prediction with Partial Labelling through the Infimum Loss. 1230-1239 - Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau:
Online Learned Continual Compression with Adaptive Quantization Modules. 1240-1250 - Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin:
Boosted Histogram Transform for Regression. 1251-1261 - Hengrui Cai, Wenbin Lu, Rui Song:
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies. 1262-1270 - Changxiao Cai, H. Vincent Poor, Yuxin Chen:
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality. 1271-1282 - Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang:
Provably Efficient Exploration in Policy Optimization. 1283-1294 - Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Near-linear time Gaussian process optimization with adaptive batching and resparsification. 1295-1305 - Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev:
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. 1306-1316 - Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giró-i-Nieto, Jordi Torres:
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills. 1317-1327 - Asaf B. Cassel, Alon Cohen, Tomer Koren:
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently. 1328-1337 - Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jérémy Rapin, Morgane Rivière, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier:
Fully Parallel Hyperparameter Search: Reshaped Space-Filling. 1338-1348 - L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi:
Data preprocessing to mitigate bias: A maximum entropy based approach. 1349-1359 - Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil:
Meta-learning with Stochastic Linear Bandits. 1360-1370 - Duo Chai, Wei Wu, Qinghong Han, Fei Wu, Jiwei Li:
Description Based Text Classification with Reinforcement Learning. 1371-1382 - Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha:
Concise Explanations of Neural Networks using Adversarial Training. 1383-1391 - Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar:
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift. 1392-1402 - William Chan, Chitwan Saharia, Geoffrey E. Hinton, Mohammad Norouzi, Navdeep Jaitly:
Imputer: Sequence Modelling via Imputation and Dynamic Programming. 1403-1413 - Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas:
Optimizing for the Future in Non-Stationary MDPs. 1414-1425 - Kai-Hung Chang, Chin-Yi Cheng:
Learning to Simulate and Design for Structural Engineering. 1426-1436 - Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. 1437-1447 - Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
Invariant Rationalization. 1448-1458 - Satrajit Chatterjee, Alan Mishchenko:
Circuit-Based Intrinsic Methods to Detect Overfitting. 1459-1468 - Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas:
Better depth-width trade-offs for neural networks through the lens of dynamical systems. 1469-1478 - Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta:
Explainable and Discourse Topic-aware Neural Language Understanding. 1479-1488 - Lakshay Chauhan, John Alberg, Zachary C. Lipton:
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing. 1489-1499 - Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes:
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning. 1500-1509 - Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang:
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training. 1510-1519 - Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song:
Learning To Stop While Learning To Predict. 1520-1530 - Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao:
Combinatorial Pure Exploration for Dueling Bandit. 1531-1541 - Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu:
Graph Optimal Transport for Cross-Domain Alignment. 1542-1553 - Xiangning Chen, Cho-Jui Hsieh:
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization. 1554-1565 - Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth D. Forbus, Jianfeng Gao:
Mapping natural-language problems to formal-language solutions using structured neural representations. 1566-1575 - Dexiong Chen, Laurent Jacob, Julien Mairal:
Convolutional Kernel Networks for Graph-Structured Data. 1576-1586 - Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt:
Learning Flat Latent Manifolds with VAEs. 1587-1596 - Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton:
A Simple Framework for Contrastive Learning of Visual Representations. 1597-1607 - Binghong Chen, Chengtao Li, Hanjun Dai, Le Song:
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search. 1608-1616 - Ting Chen, Lala Li, Yizhou Sun:
Differentiable Product Quantization for End-to-End Embedding Compression. 1617-1626 - Yu Chen, Zhenming Liu, Bin Ren, Xin Jin:
On Efficient Constructions of Checkpoints. 1627-1636 - Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. 1637-1648 - Jessie X. T. Chen, Miles E. Lopes:
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach. 1649-1659 - Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian:
VFlow: More Expressive Generative Flows with Variational Data Augmentation. 1660-1669 - Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi:
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models. 1670-1680 - Yuwen Chen, Antonio Orvieto, Aurélien Lucchi:
An Accelerated DFO Algorithm for Finite-sum Convex Functions. 1681-1690 - Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever:
Generative Pretraining From Pixels. 1691-1703 - John Chen, Vatsal Shah, Anastasios Kyrillidis:
Negative Sampling in Semi-Supervised learning. 1704-1714 - Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang:
Optimization from Structured Samples for Coverage Functions. 1715-1724 - Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li:
Simple and Deep Graph Convolutional Networks. 1725-1735 - Yanzhi Chen, Renjie Xie, Zhanxing Zhu:
On Breaking Deep Generative Model-based Defenses and Beyond. 1736-1745 - Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar:
Automated Synthetic-to-Real Generalization. 1746-1756 - Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang:
(Locally) Differentially Private Combinatorial Semi-Bandits. 1757-1767 - Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi:
High-dimensional Robust Mean Estimation via Gradient Descent. 1768-1778 - Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin:
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. 1779-1788 - Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao:
Learning with Bounded Instance and Label-dependent Label Noise. 1789-1799 - Ching-Wei Cheng, Xingye Qiao, Guang Cheng:
Mutual Transfer Learning for Massive Data. 1800-1809 - Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Stochastic Gradient and Langevin Processes. 1810-1819 - Anoop Cherian, Shuchin Aeron:
Representation Learning via Adversarially-Contrastive Optimal Transport. 1820-1830 - Badr-Eddine Chérief-Abdellatif:
Convergence Rates of Variational Inference in Sparse Deep Learning. 1831-1842 - Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu:
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism. 1843-1854 - Rachit Chhaya, Jayesh Choudhari, Anirban Dasgupta, Supratim Shit:
Streaming Coresets for Symmetric Tensor Factorization. 1855-1865 - Rachit Chhaya, Anirban Dasgupta, Supratim Shit:
On Coresets for Regularized Regression. 1866-1876 - Ashish Chiplunkar, Sagar Sudhir Kale, Sivaramakrishnan Natarajan Ramamoorthy:
How to Solve Fair k-Center in Massive Data Models. 1877-1886 - Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon:
Fair Generative Modeling via Weak Supervision. 1887-1898 - Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse H. Engel:
Encoding Musical Style with Transformer Autoencoders. 1899-1908 - Davin Choo, Christoph Grunau, Julian Portmann, Václav Rozhon:
k-means++: few more steps yield constant approximation. 1909-1917 - Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani:
Stochastic Flows and Geometric Optimization on the Orthogonal Group. 1918-1928 - Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. 1929-1938 - Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha:
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. 1939-1951 - Aristotelis Chrysakis, Marie-Francine Moens:
Online Continual Learning from Imbalanced Data. 1952-1961 - Xu Chu, Yang Lin, Yasha Wang, Xiting Wang, Hailong Yu, Xin Gao, Qi Tong:
Distance Metric Learning with Joint Representation Diversification. 1962-1973 - Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao:
Semismooth Newton Algorithm for Efficient Projections onto ℓ1, ∞-norm Ball. 1974-1983 - Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka:
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. 1984-1994 - Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser:
Scalable and Efficient Comparison-based Search without Features. 1995-2005