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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 - Inseop Chung, Seonguk Park, Jangho Kim, Nojun Kwak:
Feature-map-level Online Adversarial Knowledge Distillation. 2006-2015 - Ferdinando Cicalese, Sergio Filho, Eduardo Sany Laber, Marco Molinaro:
Teaching with Limited Information on the Learner's Behaviour. 2016-2026 - Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis:
Deep Divergence Learning. 2027-2037 - Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon:
Model Fusion with Kullback-Leibler Divergence. 2038-2047 - Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman:
Leveraging Procedural Generation to Benchmark Reinforcement Learning. 2048-2056 - Edith Cohen, Ofir Geri, Rasmus Pagh:
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. 2057-2067 - Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth:
Healing Products of Gaussian Process Experts. 2068-2077 - Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde:
On Efficient Low Distortion Ultrametric Embedding. 2078-2088 - Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava:
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. 2089-2099 - Ronan Collobert, Awni Y. Hannun, Gabriel Synnaeve:
Word-Level Speech Recognition With a Letter to Word Encoder. 2100-2110 - Cyrille W. Combettes, Sebastian Pokutta:
Boosting Frank-Wolfe by Chasing Gradients. 2111-2121 - Vincent Conitzer, Debmalya Panigrahi, Hanrui Zhang:
Learning Opinions in Social Networks. 2122-2132 - Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. 2133-2143 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Adaptive Region-Based Active Learning. 2144-2153 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Online Learning with Dependent Stochastic Feedback Graphs. 2154-2163 - Romain Cosentino, Behnaam Aazhang:
Learnable Group Transform For Time-Series. 2164-2173 - Rixon Crane, Fred Roosta:
DINO: Distributed Newton-Type Optimization Method. 2174-2184 - Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel:
Causal Modeling for Fairness In Dynamical Systems. 2185-2195 - Francesco Croce, Matthias Hein:
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. 2196-2205 - Francesco Croce, Matthias Hein:
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. 2206-2216 - Lorenzo Croissant, Marc Abeille, Clément Calauzènes:
Real-Time Optimisation for Online Learning in Auctions. 2217-2226 - Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang:
Privately detecting changes in unknown distributions. 2227-2237 - Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins:
Flexible and Efficient Long-Range Planning Through Curious Exploration. 2238-2249 - Ashok Cutkosky:
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning. 2250-2259 - Ashok Cutkosky, Harsh Mehta:
Momentum Improves Normalized SGD. 2260-2268 - Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert:
Supervised Quantile Normalization for Low Rank Matrix Factorization. 2269-2279 - Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala:
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime. 2280-2290 - Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho:
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. 2291-2301 - Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. 2302-2312 - Bin Dai, Ziyu Wang, David P. Wipf:
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. 2313-2322 - Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. 2323-2334 - Soham Dan, Bhaswar B. Bhattacharya:
Goodness-of-Fit Tests for Inhomogeneous Random Graphs. 2335-2344 - Chen Dan, Yuting Wei, Pradeep Ravikumar:
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. 2345-2355 - Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev:
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. 2356-2365 - Yehuda Dar, Paul M. Mayer, Lorenzo Luzi, Richard G. Baraniuk:
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. 2366-2375 - Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Josh Abramson, Alden Hung, Arun Ahuja, Stephen Clark, Greg Wayne, Felix Hill:
Probing Emergent Semantics in Predictive Agents via Question Answering. 2376-2391 - Trevor Davis, Martin Schmid, Michael Bowling:
Low-Variance and Zero-Variance Baselines for Extensive-Form Games. 2392-2401 - Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter:
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. 2402-2411 - Justin DeBenedetto, David Chiang:
Representing Unordered Data Using Complex-Weighted Multiset Automata. 2412-2420 - Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang:
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. 2421-2431 - Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko:
Gamification of Pure Exploration for Linear Bandits. 2432-2442 - Rémy Degenne, Han Shao, Wouter M. Koolen:
Structure Adaptive Algorithms for Stochastic Bandits. 2443-2452 - Ian A. Delbridge, David Bindel, Andrew Gordon Wilson:
Randomly Projected Additive Gaussian Processes for Regression. 2453-2463 - Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang:
Interpreting Robust Optimization via Adversarial Influence Functions. 2464-2473 - Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin:
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. 2474-2483 - Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su:
Towards Understanding the Dynamics of the First-Order Adversaries. 2484-2493 - Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni:
Robust Pricing in Dynamic Mechanism Design. 2494-2503 - Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban:
A Swiss Army Knife for Minimax Optimal Transport. 2504-2513 - Sofien Dhouib, Ievgen Redko, Carole Lartizien:
Margin-aware Adversarial Domain Adaptation with Optimal Transport. 2514-2524 - Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss:
Enhancing Simple Models by Exploiting What They Already Know. 2525-2534 - Lijun Ding, Yingjie Fei, Qiantong Xu, Chengrun Yang:
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence. 2535-2544 - Liang Ding, Rui Tuo, Shahin Shahrampour:
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. 2545-2555 - Hu Ding, Zixiu Wang:
Layered Sampling for Robust Optimization Problems. 2556-2566 - Nemanja Djuric, Zhuang Wang, Slobodan Vucetic:
Growing Adaptive Multi-hyperplane Machines. 2567-2576 - Nikita Doikov, Yurii E. Nesterov:
Inexact Tensor Methods with Dynamic Accuracies. 2577-2586 - Justin Domke:
Provable Smoothness Guarantees for Black-Box Variational Inference. 2587-2596 - Jinshuo Dong, David Durfee, Ryan Rogers:
Optimal Differential Privacy Composition for Exponential Mechanisms. 2597-2606 - Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou:
Multinomial Logit Bandit with Low Switching Cost. 2607-2615 - Chengyu Dong, Liyuan Liu, Zichao Li, Jingbo Shang:
Towards Adaptive Residual Network Training: A Neural-ODE Perspective. 2616-2626 - Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma:
On the Expressivity of Neural Networks for Deep Reinforcement Learning. 2627-2637 - Zhe Dong, Bryan A. Seybold, Kevin Murphy, Hung H. Bui:
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems. 2638-2647 - Chaosheng Dong, Bo Zeng:
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms. 2648-2657 - Yoel Drori, Ohad Shamir:
The Complexity of Finding Stationary Points with Stochastic Gradient Descent. 2658-2667 - Alexey Drutsa:
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer. 2668-2677 - Alexey Drutsa:
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders. 2678-2689 - Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler:
NGBoost: Natural Gradient Boosting for Probabilistic Prediction. 2690-2700 - Yaqi Duan, Zeyu Jia, Mengdi Wang:
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation. 2701-2709 - Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh:
Online Bayesian Moment Matching based SAT Solver Heuristics. 2710-2719 - Boyan Duan, Aaditya Ramdas, Larry A. Wasserman:
Familywise Error Rate Control by Interactive Unmasking. 2720-2729 - Abhimanyu Dubey, Alex 'Sandy' Pentland:
Cooperative Multi-Agent Bandits with Heavy Tails. 2730-2739 - Abhimanyu Dubey, Alex 'Sandy' Pentland:
Kernel Methods for Cooperative Multi-Agent Contextual Bandits. 2740-2750 - Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. 2751-2760 - Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Josh M. Susskind, Qi Shan:
Equivariant Neural Rendering. 2761-2770 - Conor Durkan, Iain Murray, George Papamakarios:
On Contrastive Learning for Likelihood-free Inference. 2771-2781 - Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. 2782-2792 - Vincent Dutordoir, Nicolas Durrande, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features. 2793-2802 - Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney:
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. 2803-2813 - Pavel E. Dvurechensky, Petr Ostroukhov, Kamil Safin, Shimrit Shtern, Mathias Staudigl:
Self-Concordant Analysis of Frank-Wolfe Algorithms. 2814-2824 - Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski:
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients. 2825-2835 - Armin Eftekhari:
Training Linear Neural Networks: Non-Local Convergence and Complexity Results. 2836-2847 - Rasheed el-Bouri, David W. Eyre, Peter J. Watkinson, Tingting Zhu, David A. Clifton:
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location. 2848-2857 - Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis:
Decision Trees for Decision-Making under the Predict-then-Optimize Framework. 2858-2867 - Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith:
Revisiting Spatial Invariance with Low-Rank Local Connectivity. 2868-2879 - Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh:
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. 2880-2891 - Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher:
Generalization Error of Generalized Linear Models in High Dimensions. 2892-2901 - Alina Ene, Huy L. Nguyen:
Parallel Algorithm for Non-Monotone DR-Submodular Maximization. 2902-2911 - Nicolai Engelmann, Dominik Linzner, Heinz Koeppl:
Continuous Time Bayesian Networks with Clocks. 2912-2921 - Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry:
Identifying Statistical Bias in Dataset Replication. 2922-2932 - Nima Eshraghi, Ben Liang:
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent. 2933-2942 - Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen:
Rigging the Lottery: Making All Tickets Winners. 2943-2952 - Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang:
Faster Graph Embeddings via Coarsening. 2953-2963 - Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino:
Latent Bernoulli Autoencoder. 2964-2974 - Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati:
Optimal Sequential Maximization: One Interview is Enough! 2975-2984 - Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu:
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory. 2985-2995 - Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang:
On hyperparameter tuning in general clustering problemsm. 2996-3007 - Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander:
Online mirror descent and dual averaging: keeping pace in the dynamic case. 3008-3017 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Stochastic Regret Minimization in Extensive-Form Games. 3018-3028 - Farzan Farnia, Asuman E. Ozdaglar:
Do GANs always have Nash equilibria? 3029-3039 - Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve:
Growing Action Spaces. 3040-3051 - Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq:
Improved Optimistic Algorithms for Logistic Bandits. 3052-3060 - William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. 3061-3071 - Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. 3072-3081 - Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov:
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models. 3082-3091 - Zhe Feng, David C. Parkes, Haifeng Xu:
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation. 3092-3101 - Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu:
Accountable Off-Policy Evaluation With Kernel Bellman Statistics. 3102-3111 - Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton:
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. 3112-3122 - Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra:
Why Are Learned Indexes So Effective? 3123-3132 - Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff:
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study. 3133-3144 - Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? 3145-3153 - Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman:
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. 3154-3164 - Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson:
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. 3165-3176 - Johannes Fischer, Ömer Sahin Tas:
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains. 3177-3187 - Dan Fisher, Mark Kozdoba, Shie Mannor:
Topic Modeling via Full Dependence Mixtures. 3188-3198 - Dylan J. Foster, Alexander Rakhlin:
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. 3199-3210 - Dylan J. Foster, Max Simchowitz:
Logarithmic Regret for Adversarial Online Control. 3211-3221 - Kimon Fountoulakis, Di Wang, Shenghao Yang:
p-Norm Flow Diffusion for Local Graph Clustering. 3222-3232 - Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari:
Stochastic Latent Residual Video Prediction. 3233-3246 - Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz:
Leveraging Frequency Analysis for Deep Fake Image Recognition. 3247-3258 - Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Linear Mode Connectivity and the Lottery Ticket Hypothesis. 3259-3269 - Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan:
No-Regret and Incentive-Compatible Online Learning. 3270-3279 - Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré:
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods. 3280-3291 - Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang:
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks. 3292-3303 - Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui:
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript. 3304-3314 - Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao:
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths. 3315-3326 - Kaito Fujii:
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions. 3327-3336 - Futoshi Futami, Issei Sato, Masashi Sugiyama:
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. 3337-3347 - Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko:
Stochastic bandits with arm-dependent delays. 3348-3356 - Alex Gain, Hava T. Siegelmann:
Abstraction Mechanisms Predict Generalization in Deep Neural Networks. 3357-3366 - Yansong Gao, Pratik Chaudhari:
A Free-Energy Principle for Representation Learning. 3367-3376 - Hongchang Gao, Heng Huang:
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? 3377-3386 - Dan Garber, Gal Korcia, Kfir Y. Levy:
Online Convex Optimization in the Random Order Model. 3387-3396 - Sankalp Garg, Aniket Bajpai, Mausam:
Symbolic Network: Generalized Neural Policies for Relational MDPs. 3397-3407 - Vikas K. Garg, Tommi S. Jaakkola:
Predicting deliberative outcomes. 3408-3418 - Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. 3419-3430 - Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, Ronnie Sircar:
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions. 3431-3441 - Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis:
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations. 3442-3451 - Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová:
Generalisation error in learning with random features and the hidden manifold model. 3452-3462 - Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos:
Black-Box Methods for Restoring Monotonicity. 3463-3473 - Pouya M. Ghari, Yanning Shen:
Online Multi-Kernel Learning with Graph-Structured Feedback. 3474-3483 - Mahsa Ghasemi, Erdem Bulgur, Ufuk Topcu:
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics. 3484-3493 - AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang:
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. 3494-3504 - Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. 3505-3514 - Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy:
Aligned Cross Entropy for Non-Autoregressive Machine Translation. 3515-3523 - Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. 3524-3534 - Amirata Ghorbani, Michael P. Kim, James Zou:
A Distributional Framework For Data Valuation. 3535-3544 - Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang:
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. 3545-3555 - Dibya Ghosh, Marc G. Bellemare:
Representations for Stable Off-Policy Reinforcement Learning. 3556-3565 - Alex Gittens, Kareem S. Aggour, Bülent Yener:
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition. 3566-3575 - Abhiram Gnanasambandam, Stanley H. Chan:
One Size Fits All: Can We Train One Denoiser for All Noise Levels? 3576-3586 - Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. 3587-3596 - Tomer Golany, Kira Radinsky, Daniel Freedman:
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification. 3597-3606 - Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein:
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. 3607-3616 - Eugene A. Golikov:
Towards a General Theory of Infinite-Width Limits of Neural Classifiers. 3617-3626 - Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin:
Differentially Private Set Union. 3627-3636 - Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous categorical: a novel simplex-valued exponential family. 3637-3647 - Maria I. Gorinova, Dave Moore, Matthew D. Hoffman:
Automatic Reparameterisation of Probabilistic Programs. 3648-3657 - Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez:
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. 3658-3667 - Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam M. J. Thomas, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio:
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. 3668-3679 - Olivier Gouvert, Thomas Oberlin, Cédric Févotte:
Ordinal Non-negative Matrix Factorization for Recommendation. 3680-3689 - Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan T. Chakaravarthy, Yogish Sabharwal, Ashish Verma:
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination. 3690-3699 - Ankit Goyal, Jia Deng:
PackIt: A Virtual Environment for Geometric Planning. 3700-3710 - Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. 3711-3721 - Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski:
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase. 3722-3731 - Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel:
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. 3732-3747 - Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo:
On the Iteration Complexity of Hypergradient Computation. 3748-3758 - Daniel Greenfeld, Uri Shalit:
Robust Learning with the Hilbert-Schmidt Independence Criterion. 3759-3768 - Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos:
Monte-Carlo Tree Search as Regularized Policy Optimization. 3769-3778 - Allan Grønlund, Lior Kamma, Kasper Green Larsen:
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines. 3779-3788 - Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman:
Implicit Geometric Regularization for Learning Shapes. 3789-3799 - Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu:
Improving the Gating Mechanism of Recurrent Neural Networks. 3800-3809 - Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou:
Recurrent Hierarchical Topic-Guided RNN for Language Generation. 3810-3821 - Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan:
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search. 3822-3831 - Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. 3832-3842 - Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao:
LTF: A Label Transformation Framework for Correcting Label Shift. 3843-3853 - Pengsheng Guo, Chen-Yu Lee, Daniel Ulbricht:
Learning to Branch for Multi-Task Learning. 3854-3863 - Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. 3864-3874 - Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar:
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. 3875-3886 - Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar:
Accelerating Large-Scale Inference with Anisotropic Vector Quantization. 3887-3896 - Lan-Zhe Guo, Zhenyu Zhang, Yuan Jiang, Yufeng Li, Zhi-Hua Zhou:
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data. 3897-3906 - Pankaj Gupta, Yatin Chaudhary, Thomas A. Runkler, Hinrich Schütze:
Neural Topic Modeling with Continual Lifelong Learning. 3907-3917 - Maya R. Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao:
Multidimensional Shape Constraints. 3918-3928 - Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang:
Retrieval Augmented Language Model Pre-Training. 3929-3938 - Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi:
Streaming Submodular Maximization under a k-Set System Constraint. 3939-3949 - Guy Hacohen, Leshem Choshen, Daphna Weinshall:
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets. 3950-3960 - Marwa El Halabi, Stefanie Jegelka:
Optimal approximation for unconstrained non-submodular minimization. 3961-3972 - Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh:
FedBoost: A Communication-Efficient Algorithm for Federated Learning. 3973-3983 - Insu Han, Haim Avron, Jinwoo Shin:
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. 3984-3993 - Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker:
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images. 3994-4005 - Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. 4006-4016 - Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu:
Training Binary Neural Networks through Learning with Noisy Supervision. 4017-4026 - Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik:
Stochastic Subspace Cubic Newton Method. 4027-4038 - Filip Hanzely, Dmitry Kovalev, Peter Richtárik:
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. 4039-4048 - Yi Hao, Alon Orlitsky:
Data Amplification: Instance-Optimal Property Estimation. 4049-4059 - Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai:
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising. 4060-4070 - Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Improving generalization by controlling label-noise information in neural network weights. 4071-4081 - Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu:
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. 4082-4093 - Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian:
Bayesian Graph Neural Networks with Adaptive Connection Sampling. 4094-4104 - Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel:
CoMic: Complementary Task Learning & Mimicry for Reusable Skills. 4105-4115 - Kaveh Hassani, Amir Hosein Khas Ahmadi:
Contrastive Multi-View Representation Learning on Graphs. 4116-4126 - Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa:
Nested Subspace Arrangement for Representation of Relational Data. 4127-4137 - Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder:
The Tree Ensemble Layer: Differentiability meets Conditional Computation. 4138-4148 - Reinhard Heckel, Mahdi Soltanolkotabi:
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation. 4149-4158 - Donald J. Hejna III, Lerrel Pinto, Pieter Abbeel:
Hierarchically Decoupled Imitation For Morphological Transfer. 4159-4171 - Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou:
Gradient-free Online Learning in Continuous Games with Delayed Rewards. 4172-4181 - Olivier J. Hénaff:
Data-Efficient Image Recognition with Contrastive Predictive Coding. 4182-4192 - Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama:
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model. 4193-4202 - Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis R. Bach, Laurent Massoulié:
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. 4203-4227 - Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang:
Cost-Effective Interactive Attention Learning with Neural Attention Processes. 4228-4238 - Joeri Hermans, Volodimir Begy, Gilles Louppe:
Likelihood-free MCMC with Amortized Approximate Ratio Estimators. 4239-4248 - Fabian Hinder, André Artelt, Barbara Hammer:
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). 4249-4259 - Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain:
Optimization and Analysis of the pAp@k Metric for Recommender Systems. 4260-4270 - Minh Hoang, Carleton Kingsford:
Optimizing Dynamic Structures with Bayesian Generative Search. 4271-4281 - Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. 4282-4292 - Quan Hoang, Trung Le, Dinh Phung:
Parameterized Rate-Distortion Stochastic Encoder. 4293-4303 - Christoph D. Hofer, Florian Graf, Marc Niethammer, Roland Kwitt:
Topologically Densified Distributions. 4304-4313 - Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt:
Graph Filtration Learning. 4314-4323 - Matthew D. Hoffman, Yian Ma:
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. 4324-4341 - Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis:
Learning Mixtures of Graphs from Epidemic Cascades. 4342-4352 - Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten M. Borgwardt:
Set Functions for Time Series. 4353-4363 - Andrea Hornáková, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda:
Lifted Disjoint Paths with Application in Multiple Object Tracking. 4364-4375 - Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak:
Infinite attention: NNGP and NTK for deep attention networks. 4376-4386 - Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons:
The Non-IID Data Quagmire of Decentralized Machine Learning. 4387-4398 - Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster:
"Other-Play" for Zero-Shot Coordination. 4399-4410 - Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson:
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation. 4411-4421 - Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Momentum-Based Policy Gradient Methods. 4422-4433 - Jiawei Huang, Nan Jiang:
From Importance Sampling to Doubly Robust Policy Gradient. 4434-4443 - Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse:
Evaluating Lossy Compression Rates of Deep Generative Models. 4444-4454 - Wenlong Huang, Igor Mordatch, Deepak Pathak:
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. 4455-4464 - Long-Kai Huang, Sinno Jialin Pan:
Communication-Efficient Distributed PCA by Riemannian Optimization. 4465-4474 - Xiao Shi Huang, Felipe Pérez, Jimmy Ba, Maksims Volkovs:
Improving Transformer Optimization Through Better Initialization. 4475-4483 - Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang:
More Information Supervised Probabilistic Deep Face Embedding Learning. 4484-4494 - Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik:
Generating Programmatic Referring Expressions via Program Synthesis. 4495-4506 - Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora:
InstaHide: Instance-hiding Schemes for Private Distributed Learning. 4507-4518 - Feihu Huang, Lue Tao, Songcan Chen:
Accelerated Stochastic Gradient-free and Projection-free Methods. 4519-4530 - Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang:
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition. 4531-4541 - Jiaoyang Huang, Horng-Tzer Yau:
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. 4542-4551 - Dongsung Huh:
Curvature-corrected learning dynamics in deep neural networks. 4552-4560 - Tri Huynh, Michael Maire, Matthew R. Walter:
Multigrid Neural Memory. 4561-4571 - Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari:
Meta-Learning with Shared Amortized Variational Inference. 4572-4582 - Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas:
Linear Lower Bounds and Conditioning of Differentiable Games. 4583-4593 - Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima:
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. 4594-4603 - Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? 4604-4614 - Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson:
Semi-Supervised Learning with Normalizing Flows. 4615-4630 - Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel:
Implicit Regularization of Random Feature Models. 4631-4640 - Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev:
Correlation Clustering with Asymmetric Classification Errors. 4641-4650 - Ayush Jain, Alon Orlitsky:
Optimal Robust Learning of Discrete Distributions from Batches. 4651-4660 - Ayush Jain, Andrew Szot, Joseph J. Lim:
Generalization to New Actions in Reinforcement Learning. 4661-4672 - Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker:
Tails of Lipschitz Triangular Flows. 4673-4681 - Steven James, Benjamin Rosman, George Konidaris:
Learning Portable Representations for High-Level Planning. 4682-4691 - Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Debiased Sinkhorn barycenters. 4692-4701 - Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Parametric Gaussian Process Regressors. 4702-4712 - Daniel Jarrett, Mihaela van der Schaar:
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making. 4713-4723 - Vivek Jayaram, John Thickstun:
Source Separation with Deep Generative Priors. 4724-4735 - Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna:
Extra-gradient with player sampling for faster convergence in n-player games. 4736-4745 - Hyeonseong Jeon, Youngoh Bang, Junyaup Kim, Simon S. Woo:
T-GD: Transferable GAN-generated Images Detection Framework. 4746-4761 - Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang:
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. 4762-4772 - Zhiwei Jia, Hao Su:
Information-Theoretic Local Minima Characterization and Regularization. 4773-4783 - Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta:
Optimizing Black-box Metrics with Adaptive Surrogates. 4784-4793 - Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett:
BINOCULARS for efficient, nonmyopic sequential experimental design. 4794-4803 - Lu Jiang, Di Huang, Mason Liu, Weilong Yang:
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels. 4804-4815 - Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei:
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation. 4816-4827 - Yibo Jiang, Cengiz Pehlevan:
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders. 4828-4838 - Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Hierarchical Generation of Molecular Graphs using Structural Motifs. 4839-4848 - Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Multi-Objective Molecule Generation using Interpretable Substructures. 4849-4859 - Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu:
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. 4860-4869 - Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu:
Reward-Free Exploration for Reinforcement Learning. 4870-4879 - Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? 4880-4889 - Yujia Jin, Aaron Sidford:
Efficiently Solving MDPs with Stochastic Mirror Descent. 4890-4900 - Ying Jin, Zhaoran Wang, Junwei Lu:
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model. 4901-4910 - Tyler B. Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin:
AdaScale SGD: A User-Friendly Algorithm for Distributed Training. 4911-4920 - Rie Johnson, Tong Zhang:
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. 4921-4930 - Alexia Jolicoeur-Martineau:
On Relativistic f-Divergences. 4931-4939 - Matthew Jones, Huy L. Nguyen, Thy Dinh Nguyen:
Fair k-Centers via Maximum Matching. 4940-4949 - Taejong Joo, Uijung Chung, Min-Gwan Seo:
Being Bayesian about Categorical Probability. 4950-4961 - Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas:
Evaluating the Performance of Reinforcement Learning Algorithms. 4962-4973 - Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. 4974-4983 - Pooria Joulani, Anant Raj, András György, Csaba Szepesvári:
A simpler approach to accelerated optimization: iterative averaging meets optimism. 4984-4993 - Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman:
Sets Clustering. 4994-5005 - Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever:
Distribution Augmentation for Generative Modeling. 5006-5019 - Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar:
Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning. 5020-5030 - Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola:
Partial Trace Regression and Low-Rank Kraus Decomposition. 5031-5041 - Anson Kahng, Gregory Kehne, Ariel D. Procaccia:
Strategyproof Mean Estimation from Multiple-Choice Questions. 5042-5052 - Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg:
Variational Autoencoders with Riemannian Brownian Motion Priors. 5053-5066 - Nathan Kallus:
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. 5067-5077 - Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. 5078-5088 - Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. 5089-5100 - Akshay Kamath, Eric Price, Sushrut Karmalkar:
On the Power of Compressed Sensing with Generative Models. 5101-5109 - Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi:
Learning and Evaluating Contextual Embedding of Source Code. 5110-5121 - Minsoo Kang, Bohyung Han:
Operation-Aware Soft Channel Pruning using Differentiable Masks. 5122-5131 - Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. 5132-5143 - Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu:
Non-autoregressive Machine Translation with Disentangled Context Transformer. 5144-5155 - Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret:
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. 5156-5165 - Keizo Kato, Jing Zhou, Tomotake Sasaki, Akira Nakagawa:
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space. 5166-5176 - Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow:
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. 5177-5186 - Iordanis Kerenidis, Alessandro Luongo, Anupam Prakash:
Quantum Expectation-Maximization for Gaussian mixture models. 5187-5197 - Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig:
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. 5198-5208 - Fereshte Khani, Percy Liang:
Feature Noise Induces Loss Discrepancy Across Groups. 5209-5219 - Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni:
Entropy Minimization In Emergent Languages. 5220-5230 - Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low:
Private Outsourced Bayesian Optimization. 5231-5242 - Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. 5243-5253 - Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar:
Uniform Convergence of Rank-weighted Learning. 5254-5263 - Joon Sik Kim, Jiahao Chen, Ameet Talwalkar:
FACT: A Diagnostic for Group Fairness Trade-offs. 5264-5274 - Jang-Hyun Kim, Wonho Choo, Hyun Oh Song:
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup. 5275-5285 - Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon:
Domain Adaptive Imitation Learning. 5286-5295 - Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
Variational Inference for Sequential Data with Future Likelihood Estimates. 5296-5305 - Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins:
Active World Model Learning with Progress Curiosity. 5306-5315 - Steven Kleinegesse, Michael U. Gutmann:
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. 5316-5326 - Jeremias Knoblauch, Hisham Husain, Tom Diethe:
Optimal Continual Learning has Perfect Memory and is NP-hard. 5327-5337 - Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang:
Concept Bottleneck Models. 5338-5348 - Georg Kohl, Kiwon Um, Nils Thuerey:
Learning Similarity Metrics for Numerical Simulations. 5349-5360 - Jonas Köhler, Leon Klein, Frank Noé:
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. 5361-5370 - Andrey Kolobov, Sébastien Bubeck, Julian Zimmert:
Online Learning for Active Cache Synchronization. 5371-5380 - Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. 5381-5393 - Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh:
Meta-learning for Mixed Linear Regression. 5394-5404 - Lingkai Kong, Jimeng Sun, Chao Zhang:
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates. 5405-5415 - Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. 5416-5425 - James E. Kostas, Chris Nota, Philip S. Thomas:
Asynchronous Coagent Networks. 5426-5435 - Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. 5436-5446 - Anurag Kumar, Vamsi K. Ithapu:
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition. 5447-5457 - Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. 5458-5467 - Ananya Kumar, Tengyu Ma, Percy Liang:
Understanding Self-Training for Gradual Domain Adaptation. 5468-5479 - Abhishek Kumar, Ben Poole:
On Implicit Regularization in β-VAEs. 5480-5490 - I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, Sorelle A. Friedler:
Problems with Shapley-value-based explanations as feature importance measures. 5491-5500 - Daniel Kumor, Carlos Cinelli, Elias Bareinboim:
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. 5501-5510 - Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. 5511-5521 - Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Online Dense Subgraph Discovery via Blurred-Graph Feedback. 5522-5532 - Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William M. Leiserson, Sage Moore, Nir Shavit, Dan Alistarh:
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. 5533-5543 - Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi:
Soft Threshold Weight Reparameterization for Learnable Sparsity. 5544-5555 - Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov:
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. 5556-5566 - Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik:
Principled learning method for Wasserstein distributionally robust optimization with local perturbations. 5567-5576 - Prashanth L. A., Krishna P. Jagannathan, Ravi Kumar Kolla:
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. 5577-5586 - Jonathan Lacotte, Mert Pilanci:
Optimal Randomized First-Order Methods for Least-Squares Problems. 5587-5597 - Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc:
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. 5598-5607 - Zehua Lai, Lek-Heng Lim:
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False. 5608-5617 - Hang Lai, Jian Shen, Weinan Zhang, Yong Yu:
Bidirectional Model-based Policy Optimization. 5618-5627 - Himabindu Lakkaraju, Nino Arsov, Osbert Bastani:
Robust and Stable Black Box Explanations. 5628-5638 - Michael Laskin, Aravind Srinivas, Pieter Abbeel:
CURL: Contrastive Unsupervised Representations for Reinforcement Learning. 5639-5650 - Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher:
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks. 5651-5661 - Tor Lattimore, Csaba Szepesvári, Gellért Weisz:
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. 5662-5670 - Hien Le, Nicolas Gillis, Panagiotis Patrinos:
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization. 5671-5681 - Hung Le, Truyen Tran, Svetha Venkatesh:
Self-Attentive Associative Memory. 5682-5691 - Sanghack Lee, Elias Bareinboim:
Causal Effect Identifiability under Partial-Observability. 5692-5701 - Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel:
Estimating Model Uncertainty of Neural Networks in Sparse Information Form. 5702-5713 - Hankook Lee, Sung Ju Hwang, Jinwoo Shin:
Self-supervised Label Augmentation via Input Transformations. 5714-5724 - Byung-Jun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim:
Batch Reinforcement Learning with Hyperparameter Gradients. 5725-5735 - Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. 5736-5746 - Sang-Hyun Lee, Seung-Woo Seo:
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning. 5747-5756 - Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin:
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning. 5757-5766 - Changhee Lee, Mihaela van der Schaar:
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression. 5767-5777 - Chanwoo Lee, Miaoyan Wang:
Tensor denoising and completion based on ordinal observations. 5778-5788 - Jiabao Lei, Kui Jia:
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks. 5789-5798 - Qi Lei, Jason D. Lee, Alex Dimakis, Constantinos Daskalakis:
SGD Learns One-Layer Networks in WGANs. 5799-5808 - Yunwen Lei, Yiming Ying:
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. 5809-5819 - Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland:
Learning Quadratic Games on Networks. 5820-5830 - Yang Li, Shoaib Akbar, Junier Oliva:
ACFlow: Flow Models for Arbitrary Conditional Likelihoods. 5831-5841 - Yu-Sheng Li, Wei-Lin Chiang, Ching-Pei Lee:
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. 5842-5852 - Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu:
Neural Architecture Search in A Proxy Validation Loss Landscape. 5853-5862 - Shiyu Li, Edward Hanson, Hai Li, Yiran Chen:
PENNI: Pruned Kernel Sharing for Efficient CNN Inference. 5863-5873 - Mingjie Li, Lingshen He, Zhouchen Lin:
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability. 5874-5883 - Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu:
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. 5884-5894 - Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik:
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. 5895-5904 - Jianing Li, Yanyan Lan, Jiafeng Guo, Xueqi Cheng:
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation. 5905-5915 - Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin:
Latent Space Factorisation and Manipulation via Matrix Subspace Projection. 5916-5926 - Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba:
Visual Grounding of Learned Physical Models. 5927-5936 - Steven Cheng-Xian Li, Benjamin M. Marlin:
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective. 5937-5946 - Chao Li, Zhun Sun:
Evolutionary Topology Search for Tensor Network Decomposition. 5947-5957 - Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joey Gonzalez:
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. 5958-5968 - Bingcong Li, Lingda Wang, Georgios B. Giannakis:
Almost Tune-Free Variance Reduction. 5969-5978 - Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang:
Nearly Linear Row Sampling Algorithm for Quantile Regression. 5979-5989 - Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song:
Temporal Logic Point Processes. 5990-6000 - Yi Li, David P. Woodruff:
Input-Sparsity Low Rank Approximation in Schatten Norm. 6001-6009 - Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou:
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr. 6010-6019 - Zhimei Li, Yaowu Zhang:
On a projective ensemble approach to two sample test for equality of distributions. 6020-6027 - Jian Liang, Dapeng Hu, Jiashi Feng:
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation. 6028-6039 - Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen:
Variable Skipping for Autoregressive Range Density Estimation. 6040-6049 - Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard B. Fair:
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning. 6050-6060 - Jae Hyun Lim, Aaron C. Courville, Christopher J. Pal, Chin-Wei Huang:
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. 6061-6071 - Cong Han Lim, Raquel Urtasun, Ersin Yumer:
Hierarchical Verification for Adversarial Robustness. 6072-6082 - Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. 6083-6093 - Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. 6094-6104 - Qiaohui Lin, Robert Lunde, Purnamrita Sarkar:
On the Theoretical Properties of the Network Jackknife. 6105-6115 - Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan:
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. 6116-6126 - Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs. 6127-6139 - Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn:
Improving Generative Imagination in Object-Centric World Models. 6140-6149 - Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer:
Generalized and Scalable Optimal Sparse Decision Trees. 6150-6160 - Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. 6161-6171 - Vasileios Lioutas, Yuhong Guo:
Time-aware Large Kernel Convolutions. 6172-6183 - Yao Liu, Pierre-Luc Bacon, Emma Brunskill:
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. 6184-6193 - Guodong Liu, Hong Chen, Heng Huang:
Sparse Shrunk Additive Models. 6194-6204 - Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie:
Boosting Deep Neural Network Efficiency with Dual-Module Inference. 6205-6215 - Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett:
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. 6216-6225 - Yang Liu, Hongyi Guo:
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. 6226-6236 - Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn:
An Imitation Learning Approach for Cache Replacement. 6237-6247 - Xi Liu, Ping-Chun Hsieh, Yu-Heng Hung, Anirban Bhattacharya, P. R. Kumar:
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits. 6248-6258 - Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar:
Hallucinative Topological Memory for Zero-Shot Visual Planning. 6259-6270 - Xianggen Liu, Qiang Liu, Sen Song, Jian Peng:
A Chance-Constrained Generative Framework for Sequence Optimization. 6271-6281 - Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly:
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks. 6282-6293 - Weidong Liu, Xiaojun Mao, Raymond K. W. Wong:
Median Matrix Completion: from Embarrassment to Optimality. 6294-6304 - Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang:
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton. 6305-6315 - Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. 6316-6326 - Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. 6327-6335 - Tianlin Liu, Friedemann Zenke:
Finding trainable sparse networks through Neural Tangent Transfer. 6336-6347 - Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. 6348-6359 - Michael Lohaus, Michaël Perrot, Ulrike von Luxburg:
Too Relaxed to Be Fair. 6360-6369 - Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. 6370-6381 - Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney:
Error Estimation for Sketched SVD via the Bootstrap. 6382-6392 - Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa:
Differentiating through the Fréchet Mean. 6393-6403 - Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew J. Hausknecht:
Working Memory Graphs. 6404-6414 - Yucheng Lu, Christopher De Sa:
Moniqua: Modulo Quantized Communication in Decentralized SGD. 6415-6425 - Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying:
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth. 6426-6436 - Yuchen Lu, Soumye Singhal, Florian Strub, Aaron C. Courville, Olivier Pietquin:
Countering Language Drift with Seeded Iterated Learning. 6437-6447 - Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? 6448-6458 - Siqiang Luo:
Improved Communication Cost in Distributed PageRank Computation - A Theoretical Study. 6459-6467 - Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh:
Progressive Graph Learning for Open-Set Domain Adaptation. 6468-6478 - Lei Luo, Yanfu Zhang, Heng Huang:
Adversarial Nonnegative Matrix Factorization. 6479-6488 - Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh:
Learning Algebraic Multigrid Using Graph Neural Networks. 6489-6499 - Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. 6500-6510 - Thodoris Lykouris, Vahab S. Mirrokni, Renato Paes Leme:
Bandits with Adversarial Scaling. 6511-6521 - Pingchuan Ma, Tao Du, Wojciech Matusik:
Efficient Continuous Pareto Exploration in Multi-Task Learning. 6522-6531 - Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang:
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. 6532-6542 - Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah M. Erfani, James Bailey:
Normalized Loss Functions for Deep Learning with Noisy Labels. 6543-6553 - Runchao Ma, Qihang Lin, Tianbao Yang:
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. 6554-6564 - Shaocong Ma, Yi Zhou:
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. 6565-6574 - Divyam Madaan, Jinwoo Shin, Sung Ju Hwang:
Adversarial Neural Pruning with Latent Vulnerability Suppression. 6575-6585 - Sepideh Mahabadi, Ali Vakilian:
Individual Fairness for k-Clustering. 6586-6596 - Debabrata Mahapatra, Vaibhav Rajan:
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization. 6597-6607 - Niru Maheswaranathan, David Sussillo:
How recurrent networks implement contextual processing in sentiment analysis. 6608-6619 - Vien V. Mai, Mikael Johansson:
Anderson Acceleration of Proximal Gradient Methods. 6620-6629 - Vien V. Mai, Mikael Johansson:
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization. 6630-6639 - Pratyush Maini, Eric Wong, J. Zico Kolter:
Adversarial Robustness Against the Union of Multiple Perturbation Models. 6640-6650 - Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer:
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. 6651-6660 - Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Bounds on Potential Outcomes For Decision Making. 6661-6671 - Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason D. Lee:
Optimal transport mapping via input convex neural networks. 6672-6681 - Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. 6682-6691 - Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed-Point Methods for Federated Learning. 6692-6701 - Yura Malitsky, Konstantin Mishchenko:
Adaptive Gradient Descent without Descent. 6702-6712 - Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung:
Emergence of Separable Manifolds in Deep Language Representations. 6713-6723 - Yuren Mao, Weiwei Liu, Xuemin Lin:
Adaptive Adversarial Multi-task Representation Learning. 6724-6733 - Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. 6734-6744 - John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan J. Englot:
Stochastically Dominant Distributional Reinforcement Learning. 6745-6754 - Natalia Martínez, Martín Bertrán, Guillermo Sapiro:
Minimax Pareto Fairness: A Multi Objective Perspective. 6755-6764 - Charles T. Marx, Flávio P. Calmon, Berk Ustun:
Predictive Multiplicity in Classification. 6765-6774 - Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith:
Adding seemingly uninformative labels helps in low data regimes. 6775-6784 - Robert Mattila, Cristian R. Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg:
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations. 6785-6796 - Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. 6797-6807 - Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner:
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification. 6808-6819 - Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. 6820-6829 - Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester:
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM. 6830-6840 - Celestine Mendler-Dünner, Aurélien Lucchi:
Randomized Block-Diagonal Preconditioning for Parallel Learning. 6841-6851 - Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan:
Training Binary Neural Networks using the Bayesian Learning Rule. 6852-6861 - Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli:
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning. 6862-6873 - Francesca Mignacco, Florent Krzakala, Yue Lu, Pierfrancesco Urbani, Lenka Zdeborová:
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture. 6874-6883 - Petrus Mikkola, Milica Todorovic, Jari Järvi, Patrick Rinke, Samuel Kaski:
Projective Preferential Bayesian Optimization. 6884-6892 - Zoltán Ádám Milacski, Barnabás Póczos, András Lörincz:
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing. 6893-6904 - John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt:
The Effect of Natural Distribution Shift on Question Answering Models. 6905-6916 - John Miller, Smitha Milli, Moritz Hardt:
Strategic Classification is Causal Modeling in Disguise. 6917-6926 - Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen:
Automatic Shortcut Removal for Self-Supervised Representation Learning. 6927-6937 - Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel:
Learning Reasoning Strategies in End-to-End Differentiable Proving. 6938-6949 - Baharan Mirzasoleiman, Jeff A. Bilmes, Jure Leskovec:
Coresets for Data-efficient Training of Machine Learning Models. 6950-6960 - Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. 6961-6971 - Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio:
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. 6972-6986 - Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier:
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. 6987-6998 - Zahra Monfared, Daniel Durstewitz:
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time. 6999-7009 - Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. 7010-7021 - João Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago H. Falk:
An end-to-end approach for the verification problem: learning the right distance. 7022-7033 - Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang:
Confidence-Aware Learning for Deep Neural Networks. 7034-7044 - Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Topological Autoencoders. 7045-7054 - Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost:
Explainable k-Means and k-Medians Clustering. 7055-7065 - Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro:
Fair Learning with Private Demographic Data. 7066-7075 - Hussein Mozannar, David A. Sontag:
Consistent Estimators for Learning to Defer to an Expert. 7076-7087 - Michael Muehlebach, Michael I. Jordan:
Continuous-time Lower Bounds for Gradient-based Algorithms. 7088-7096 - Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Two Simple Ways to Learn Individual Fairness Metrics from Data. 7097-7107 - Rotem Mulayoff, Tomer Michaeli:
Unique Properties of Flat Minima in Deep Networks. 7108-7118 - Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls:
Fast computation of Nash Equilibria in Imperfect Information Games. 7119-7129 - Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi:
Missing Data Imputation using Optimal Transport. 7130-7140 - Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. 7141-7152 - Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser:
Full Law Identification in Graphical Models of Missing Data: Completeness Results. 7153-7163 - Eliya Nachmani, Yossi Adi, Lior Wolf:
Voice Separation with an Unknown Number of Multiple Speakers. 7164-7175 - Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo:
Reliable Fidelity and Diversity Metrics for Generative Models. 7176-7185 - Sai Ganesh Nagarajan, David Balduzzi, Georgios Piliouras:
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics. 7186-7196 - Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, Tijmen Blankevoort:
Up or Down? Adaptive Rounding for Post-Training Quantization. 7197-7206 - Suraj Nair, Silvio Savarese, Chelsea Finn:
Goal-Aware Prediction: Learning to Model What Matters. 7207-7219 - Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia:
PolyGen: An Autoregressive Generative Model of 3D Meshes. 7220-7229 - Ivan Nazarov, Evgeny Burnaev:
Bayesian Sparsification of Deep C-valued Networks. 7230-7242 - Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Oracle Efficient Private Non-Convex Optimization. 7243-7252 - Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa:
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. 7253-7262 - Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy:
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. 7263-7272 - Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. 7273-7282 - Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot:
Aggregation of Multiple Knockoffs. 7283-7293 - Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau:
LEEP: A New Measure to Evaluate Transferability of Learned Representations. 7294-7305 - Hoang Nguyen, Takanori Maehara:
Graph Homomorphism Convolution. 7306-7316 - Vu Nguyen, Michael A. Osborne:
Knowing The What But Not The Where in Bayesian Optimization. 7317-7326 - Viet Anh Nguyen, Nian Si, Jose H. Blanchet:
Robust Bayesian Classification Using An Optimistic Score Ratio. 7327-7337 - Lan Nguyen, My T. Thai:
Streaming k-Submodular Maximization under Noise subject to Size Constraint. 7338-7347 - Vlad Niculae, André F. T. Martins:
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction. 7348-7359 - Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. 7360-7369 - Richard Nock, Aditya Krishna Menon:
Supervised learning: no loss no cry. 7370-7380 - Alex Nowak, Francis R. Bach, Alessandro Rudi:
Consistent Structured Prediction with Max-Min Margin Markov Networks. 7381-7391 - Anton Obukhov, Maxim V. Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool:
T-Basis: a Compact Representation for Neural Networks. 7392-7404 - Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan A. Shell:
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders. 7405-7413 - Naoto Ohsaka, Tatsuya Matsuoka:
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. 7414-7423