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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 - Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski:
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning? 7424-7433 - Edouard Oyallon:
Interferometric Graph Transform: a Deep Unsupervised Graph Representation. 7434-7444 - Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan:
Learning to Score Behaviors for Guided Policy Optimization. 7445-7454 - Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski:
Neural Clustering Processes. 7455-7465 - Soumyabrata Pal, Arya Mazumdar:
Recovery of Sparse Signals from a Mixture of Linear Samples. 7466-7475 - Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li:
Adversarial Mutual Information for Text Generation. 7476-7486 - Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell:
Stabilizing Transformers for Reinforcement Learning. 7487-7498 - Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu:
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. 7499-7509 - Seong-Jin Park, Seungju Han, Ji-Won Baek, Insoo Kim, Juhwan Song, Haebeom Lee, Jae-Joon Han, Sung Ju Hwang:
Meta Variance Transfer: Learning to Augment from the Others. 7510-7520 - Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao:
Structured Policy Iteration for Linear Quadratic Regulator. 7521-7531 - François-Pierre Paty, Marco Cuturi:
Regularized Optimal Transport is Ground Cost Adversarial. 7532-7542 - Brahma S. Pavse, Ishan Durugkar, Josiah Hanna, Peter Stone:
Reducing Sampling Error in Batch Temporal Difference Learning. 7543-7552 - Fabian Pedregosa, Damien Scieur:
Acceleration through spectral density estimation. 7553-7562 - Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. 7563-7574 - Dylan Peifer, Michael Eugene Stillman, Daniel Halpern-Leistner:
Learning Selection Strategies in Buchberger's Algorithm. 7575-7585 - Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao:
Non-Autoregressive Neural Text-to-Speech. 7586-7598 - Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt:
Performative Prediction. 7599-7609 - Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei:
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks. 7610-7619 - Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko:
Budgeted Online Influence Maximization. 7620-7631 - Adeel Pervez, Taco Cohen, Efstratios Gavves:
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. 7632-7640 - Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion:
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. 7641-7651 - Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav S. Sukhatme, Vladlen Koltun:
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning. 7652-7662 - Marc E. Pfetsch, Sebastian Pokutta:
IPBoost - Non-Convex Boosting via Integer Programming. 7663-7672 - Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui:
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm. 7673-7682 - NhatHai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou:
Scalable Differential Privacy with Certified Robustness in Adversarial Learning. 7683-7694 - Mert Pilanci, Tolga Ergen:
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks. 7695-7705 - Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song:
WaveFlow: A Compact Flow-based Model for Raw Audio. 7706-7716 - Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif:
Randomization matters How to defend against strong adversarial attacks. 7717-7727 - Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi:
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. 7728-7738 - Konstantinos Pitas:
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation. 7739-7749 - Silviu Pitis, Harris Chan, Stephen Zhao, Bradly C. Stadie, Jimmy Ba:
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning. 7750-7761 - Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar:
Explaining Groups of Points in Low-Dimensional Representations. 7762-7771 - Sebastian Pokutta, Mohit Singh, Alfredo Torrico:
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. 7772-7782 - Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine:
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning. 7783-7792 - Sebastian Prillo, Julian Martin Eisenschlos:
SoftSort: A Continuous Relaxation for the argsort Operator. 7793-7802 - Liudmila Prokhorenkova, Aleksandr Shekhovtsov:
Graph-based Nearest Neighbor Search: From Practice to Theory. 7803-7813 - Muni Sreenivas Pydi, Varun S. Jog:
Adversarial Risk via Optimal Transport and Optimal Couplings. 7814-7823 - Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik:
Deep Isometric Learning for Visual Recognition. 7824-7835 - Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David D. Cox:
Unsupervised Speech Decomposition via Triple Information Bottleneck. 7836-7846 - Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin:
Scalable Differentiable Physics for Learning and Control. 7847-7856 - Shuang Qiu, Xiaohan Wei, Zhuoran Yang:
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis. 7857-7866 - Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux, Jian Tang:
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs. 7867-7876 - Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh:
DeepCoDA: personalized interpretability for compositional health data. 7877-7886 - Akbar Rafiey, Yuichi Yoshida:
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints. 7887-7897 - Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani:
Transparency Promotion with Model-Agnostic Linear Competitors. 7898-7908 - Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. 7909-7919 - Roberta Raileanu, Maxwell Goldstein, Arthur Szlam, Rob Fergus:
Fast Adaptation to New Environments via Policy-Dynamics Value Functions. 7920-7931 - Ankit Raj, Yoram Bresler, Bo Li:
Improving Robustness of Deep-Learning-Based Image Reconstruction. 7932-7942 - Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris, Christos-Savvas Bouganis:
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs. 7943-7952 - Aravind Rajeswaran, Igor Mordatch, Vikash Kumar:
A Game Theoretic Framework for Model Based Reinforcement Learning. 7953-7963 - Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos:
Closing the convergence gap of SGD without replacement. 7964-7973 - Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla:
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. 7974-7984 - Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu:
Implicit Generative Modeling for Efficient Exploration. 7985-7995 - Siamak Ravanbakhsh:
Universal Equivariant Multilayer Perceptrons. 7996-8006 - Esteban Real, Chen Liang, David R. So, Quoc V. Le:
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. 8007-8019 - Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike:
Learning Human Objectives by Evaluating Hypothetical Behavior. 8020-8029 - Henry W. J. Reeve, Ata Kabán:
Optimistic Bounds for Multi-output Learning. 8030-8040 - Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates:
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. 8041-8050 - Wenbo Ren, Jia Liu, Ness B. Shroff:
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons. 8051-8072 - Luca Rendsburg, Holger Heidrich, Ulrike von Luxburg:
NetGAN without GAN: From Random Walks to Low-Rank Approximations. 8073-8082 - Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer:
Normalizing Flows on Tori and Spheres. 8083-8092 - Leslie Rice, Eric Wong, J. Zico Kolter:
Overfitting in adversarially robust deep learning. 8093-8104 - Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. 8105-8115 - Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu:
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge. 8116-8126 - Joshua Robinson, Stefanie Jegelka, Suvrit Sra:
Strength from Weakness: Fast Learning Using Weak Supervision. 8127-8136 - Veronika Rocková:
On Semi-parametric Inference for BART. 8137-8146 - Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh:
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training. 8147-8157 - Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock:
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. 8158-8168 - Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher:
Double-Loop Unadjusted Langevin Algorithm. 8169-8177 - David Rolnick, Konrad P. Kording:
Reverse-engineering deep ReLU networks. 8178-8187 - David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. 8188-8199 - Orlando Romero, Mouhacine Benosman:
Finite-Time Convergence in Continuous-Time Optimization. 8200-8209 - Aviv Rosenberg, Alon Cohen, Yishay Mansour, Haim Kaplan:
Near-optimal Regret Bounds for Stochastic Shortest Path. 8210-8219 - Nir Rosenfeld, Kojin Oshiba, Yaron Singer:
Predicting Choice with Set-Dependent Aggregation. 8220-8229 - Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter:
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. 8230-8241 - Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Björn Ommer, Joseph Paul Cohen:
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. 8242-8252 - Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora:
FetchSGD: Communication-Efficient Federated Learning with Sketching. 8253-8265 - Václav Rozhon:
Simple and sharp analysis of k-means||. 8266-8275 - Bin Xin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J. Roberts:
Bayesian Optimisation over Multiple Continuous and Categorical Inputs. 8276-8285 - Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. 8286-8294 - Chaitanya K. Ryali, John J. Hopfield, Leopold Grinberg, Dmitry Krotov:
Bio-Inspired Hashing for Unsupervised Similarity Search. 8295-8306 - Parsa Saadatpanah, Ali Shafahi, Tom Goldstein:
Adversarial Attacks on Copyright Detection Systems. 8307-8315 - Sivan Sabato, Elad Yom-Tov:
Bounding the fairness and accuracy of classifiers from population statistics. 8316-8325 - Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou:
Radioactive data: tracing through training. 8326-8335 - Basil Saeed, Snigdha Panigrahi, Caroline Uhler:
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs. 8336-8345 - Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang:
An Investigation of Why Overparameterization Exacerbates Spurious Correlations. 8346-8356 - Aadirupa Saha, Pierre Gaillard, Michal Valko:
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. 8357-8366 - Aadirupa Saha, Aditya Gopalan:
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. 8367-8376 - Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. 8377-8387 - Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause:
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. 8388-8397 - Yuta Saito, Shota Yasui:
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. 8398-8407 - Andrey Sakryukin, Chedy Raïssi, Mohan S. Kankanhalli:
Inferring DQN structure for high-dimensional continuous control. 8408-8416 - Fariborz Salehi, Ehsan Abbasi, Babak Hassibi:
The Performance Analysis of Generalized Margin Maximizers on Separable Data. 8417-8426 - Sudeep Salgia, Qing Zhao, Sattar Vakili:
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. 8427-8437 - David Salinas, Huibin Shen, Valerio Perrone:
A Quantile-based Approach for Hyperparameter Transfer Learning. 8438-8448 - Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran:
Spectral Subsampling MCMC for Stationary Time Series. 8449-8458 - Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia:
Learning to Simulate Complex Physics with Graph Networks. 8459-8468 - Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein:
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. 8469-8479 - Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus:
Explicit Gradient Learning for Black-Box Optimization. 8480-8490 - Chandramouli Shama Sastry, Sageev Oore:
Detecting Out-of-Distribution Examples with Gram Matrices. 8491-8501 - Harsh Satija, Philip Amortila, Joelle Pineau:
Constrained Markov Decision Processes via Backward Value Functions. 8502-8511 - Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. 8512-8521 - Meyer Scetbon, Zaïd Harchaoui:
Harmonic Decompositions of Convolutional Networks. 8522-8532 - Florian Schäfer, Hongkai Zheng, Animashree Anandkumar:
Implicit competitive regularization in GANs. 8533-8544 - Simon Schmitt, Matteo Hessel, Karen Simonyan:
Off-Policy Actor-Critic with Shared Experience Replay. 8545-8554 - Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano:
Discriminative Adversarial Search for Abstractive Summarization. 8555-8564 - Damien Scieur, Fabian Pedregosa:
Universal Asymptotic Optimality of Polyak Momentum. 8565-8572 - Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet:
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. 8573-8582 - Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak:
Planning to Explore via Self-Supervised World Models. 8583-8592 - Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David G. T. Barrett, Marta Garnelo:
An Explicitly Relational Neural Network Architecture. 8593-8603 - Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor:
Optimistic Policy Optimization with Bandit Feedback. 8604-8613 - Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht:
Neural Kernels Without Tangents. 8614-8623 - Tanmay Shankar, Abhinav Gupta:
Learning Robot Skills with Temporal Variational Inference. 8624-8633 - Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt:
Evaluating Machine Accuracy on ImageNet. 8634-8644 - Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo:
Channel Equilibrium Networks for Learning Deep Representation. 8645-8654 - Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek F. Abdelzaher:
ControlVAE: Controllable Variational Autoencoder. 8655-8664 - Ibrahim El Shar, Daniel R. Jiang:
Lookahead-Bounded Q-learning. 8665-8675 - Yonadav Shavit, Benjamin L. Edelman, Brian Axelrod:
Causal Strategic Linear Regression. 8676-8686 - Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh:
Adaptive Sampling for Estimating Probability Distributions. 8687-8696 - Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma:
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions. 8697-8706 - Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao:
Deep Reinforcement Learning with Robust and Smooth Policy. 8707-8718 - Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Educating Text Autoencoders: Latent Representation Guidance via Denoising. 8719-8729 - Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. 8730-8740 - Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
PowerNorm: Rethinking Batch Normalization in Transformers. 8741-8751 - Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon:
Extreme Multi-label Classification from Aggregated Labels. 8752-8762 - Yue Sheng, Edgar Dobriban:
One-shot Distributed Ridge Regression in High Dimensions. 8763-8772 - Alexander Shevchenko, Marco Mondelli:
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks. 8773-8784 - Kensen Shi, David Bieber, Charles Sutton:
Incremental Sampling Without Replacement for Sequence Models. 8785-8795 - Yunpeng Shi, Gilad Lerman:
Message Passing Least Squares Framework and its Application to Rotation Synchronization. 8796-8806 - Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng:
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making. 8807-8817 - Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang:
A Graph to Graphs Framework for Retrosynthesis Prediction. 8818-8827 - Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang:
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. 8828-8839 - Wenxian Shi, Hao Zhou, Ning Miao, Lei Li:
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation. 8840-8851 - Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo:
On Conditional Versus Marginal Bias in Multi-Armed Bandits. 8852-8861 - Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui:
Predictive Coding for Locally-Linear Control. 8862-8871 - Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen:
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change. 8872-8883 - Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet:
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. 8884-8894 - Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama:
Piecewise Linear Regression via a Difference of Convex Functions. 8895-8904 - Umer Siddique, Paul Weng, Matthieu Zimmer:
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. 8905-8915 - Per Sidén, Fredrik Lindsten:
Deep Gaussian Markov Random Fields. 8916-8926 - Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low:
Collaborative Machine Learning with Incentive-Aware Model Rewards. 8927-8936 - Max Simchowitz, Dylan J. Foster:
Naive Exploration is Optimal for Online LQR. 8937-8948 - Gregor N. C. Simm, José Miguel Hernández-Lobato:
A Generative Model for Molecular Distance Geometry. 8949-8958 - Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato:
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. 8959-8969 - Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. 8970-8980 - Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. 8981-8991 - Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake:
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. 8992-9004 - Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena:
Small-GAN: Speeding up GAN Training using Core-Sets. 9005-9015 - John Sipple:
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. 9016-9025 - Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee:
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. 9026-9035 - Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. 9036-9045 - Leon Sixt, Maximilian Granz, Tim Landgraf:
When Explanations Lie: Why Many Modified BP Attributions Fail. 9046-9057 - Samuel L. Smith, Erich Elsen, Soham De:
On the Generalization Benefit of Noise in Stochastic Gradient Descent. 9058-9067 - Georgios Smyrnis, Petros Maragos:
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation. 9068-9077 - Jiaming Song, Stefano Ermon:
Bridging the Gap Between f-GANs and Wasserstein GANs. 9078-9087 - Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao:
Provably Efficient Model-based Policy Adaptation. 9088-9098 - Przemyslaw Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski:
Hypernetwork approach to generating point clouds. 9099-9108 - Megha Srivastava, Tatsunori B. Hashimoto, Percy Liang:
Robustness to Spurious Correlations via Human Annotations. 9109-9119 - Trevor Standley, Amir Zamir, Dawn Chen, Leonidas J. Guibas, Jitendra Malik, Silvio Savarese:
Which Tasks Should Be Learned Together in Multi-task Learning? 9120-9132 - Adam Stooke, Joshua Achiam, Pieter Abbeel:
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. 9133-9143 - Karl Stratos, Sam Wiseman:
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information. 9144-9154 - David Stutz, Matthias Hein, Bernt Schiele:
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. 9155-9166 - Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík:
Doubly robust off-policy evaluation with shrinkage. 9167-9176 - Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen:
Task Understanding from Confusing Multi-task Data. 9177-9186 - Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier:
ConQUR: Mitigating Delusional Bias in Deep Q-Learning. 9187-9195 - Yi Su, Pavithra Srinath, Akshay Krishnamurthy:
Adaptive Estimator Selection for Off-Policy Evaluation. 9196-9205 - Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, Jeffrey Clune:
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. 9206-9216 - Haoran Sun, Songtao Lu, Mingyi Hong:
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking. 9217-9228 - Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt:
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts. 9229-9248 - Zhiqing Sun, Yiming Yang:
An EM Approach to Non-autoregressive Conditional Sequence Generation. 9249-9258 - Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal:
The Shapley Taylor Interaction Index. 9259-9268 - Mukund Sundararajan, Amir Najmi:
The Many Shapley Values for Model Explanation. 9269-9278 - Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama:
Multi-objective Bayesian Optimization using Pareto-frontier Entropy. 9279-9288 - Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. 9289-9299 - Quinlan Sykora, Mengye Ren, Raquel Urtasun:
Multi-Agent Routing Value Iteration Network. 9300-9310 - Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter:
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. 9311-9323 - Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani:
Quantized Decentralized Stochastic Learning over Directed Graphs. 9324-9333 - Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama:
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. 9334-9345 - Edric Tam, David B. Dunson:
Fiedler Regularization: Learning Neural Networks with Graph Sparsity. 9346-9355 - Chong Min John Tan, Mehul Motani:
DropNet: Reducing Neural Network Complexity via Iterative Pruning. 9356-9366 - Yunhao Tang, Shipra Agrawal, Yuri Faenza:
Reinforcement Learning for Integer Programming: Learning to Cut. 9367-9376 - Wenpin Tang, Xin Guo, Fengmin Tang:
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application. 9377-9386 - Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens:
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. 9387-9396 - Yunhao Tang, Michal Valko, Rémi Munos:
Taylor Expansion Policy Optimization. 9397-9406 - Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. 9407-9417 - Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jérémie Mary:
Learning disconnected manifolds: a no GAN's land. 9418-9427 - Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric:
No-Regret Exploration in Goal-Oriented Reinforcement Learning. 9428-9437 - Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan:
Sparse Sinkhorn Attention. 9438-9447 - Komal K. Teru, Etienne G. Denis, William L. Hamilton:
Inductive Relation Prediction by Subgraph Reasoning. 9448-9457 - Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. 9458-9469 - Yuandong Tian:
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension. 9470-9480 - Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli:
Sequential Transfer in Reinforcement Learning with a Generative Model. 9481-9492 - Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba:
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions. 9493-9503 - Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh:
Multi-step Greedy Reinforcement Learning Algorithms. 9504-9513 - Kiran Tomlinson, Austin R. Benson:
Choice Set Optimization Under Discrete Choice Models of Group Decisions. 9514-9525 - Alexander Tong, Jessie Huang, Guy Wolf, David van Dijk, Smita Krishnaswamy:
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. 9526-9536 - Shruti Tople, Amit Sharma, Aditya Nori:
Alleviating Privacy Attacks via Causal Learning. 9537-9547 - Csaba Tóth, Harald Oberhauser:
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances. 9548-9560 - Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. 9561-9571 - Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen:
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. 9572-9582 - Aleksei Triastcyn, Boi Faltings:
Bayesian Differential Privacy for Machine Learning. 9583-9592 - Nilesh Tripuraneni, Lester Mackey:
Single Point Transductive Prediction. 9593-9602 - Rakshit Trivedi, Jiachen Yang, Hongyuan Zha:
GraphOpt: Learning Optimization Models of Graph Formation. 9603-9613 - Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho:
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources. 9614-9624 - Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry:
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. 9625-9635 - Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. 9636-9647 - Javier Turek, Shailee Jain, Vy A. Vo, Mihai Capota, Alexander Huth, Theodore L. Willke:
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network. 9648-9658 - Masatoshi Uehara, Jiawei Huang, Nan Jiang:
Minimax Weight and Q-Function Learning for Off-Policy Evaluation. 9659-9668 - Aleksei Ustimenko, Liudmila Prokhorenkova:
StochasticRank: Global Optimization of Scale-Free Discrete Functions. 9669-9679 - Arash Vahdat, Evgeny Andriyash, William G. Macready:
Undirected Graphical Models as Approximate Posteriors. 9680-9689 - Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal:
Uncertainty Estimation Using a Single Deep Deterministic Neural Network. 9690-9700 - Marko Vasic, Cameron T. Chalk, Sarfraz Khurshid, David Soloveichik:
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks. 9701-9711 - Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner:
Linear bandits with Stochastic Delayed Feedback. 9712-9721 - Claire Vernade, András György, Timothy A. Mann:
Non-Stationary Delayed Bandits with Intermediate Observations. 9722-9732 - Alexander Vezhnevets, Yuhuai Wu, Maria K. Eckstein, Rémi Leblond, Joel Z. Leibo:
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning. 9733-9742 - Thibaut Vidal, Maximilian Schiffer:
Born-Again Tree Ensembles. 9743-9753 - Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu:
Private Reinforcement Learning with PAC and Regret Guarantees. 9754-9764 - Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu:
New Oracle-Efficient Algorithms for Private Synthetic Data Release. 9765-9774 - Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher:
Conditional gradient methods for stochastically constrained convex minimization. 9775-9785 - Andrey Voynov, Artem Babenko:
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space. 9786-9796 - Akifumi Wachi, Yanan Sui:
Safe Reinforcement Learning in Constrained Markov Decision Processes. 9797-9806 - Chengcheng Wan, Henry Hoffmann, Shan Lu, Michael Maire:
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks. 9807-9817 - Yuanyu Wan, Wei-Wei Tu, Lijun Zhang:
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity. 9818-9828 - Haiying Wang:
Logistic Regression for Massive Data with Rare Events. 9829-9836 - Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
On the Global Optimality of Model-Agnostic Meta-Learning. 9837-9846 - Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng:
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching. 9847-9856 - Zheng Wang, Xinqi Chu, Shandian Zhe:
Self-Modulating Nonparametric Event-Tensor Factorization. 9857-9867 - Guanyi Wang, Santanu S. Dey:
Upper bounds for Model-Free Row-Sparse Principal Component Analysis. 9868-9875 - Tonghan Wang, Heng Dong, Victor R. Lesser, Chongjie Zhang:
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles. 9876-9886 - Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark F. J. Steel:
Non-separable Non-stationary random fields. 9887-9897 - Hao Wang, Hao He, Dina Katabi:
Continuously Indexed Domain Adaptation. 9898-9907 - Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich:
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach. 9908-9918 - Xin Wang, Thomas E. Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu:
Frustratingly Simple Few-Shot Object Detection. 9919-9928 - Tongzhou Wang, Phillip Isola:
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. 9929-9939 - Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, Kenneth O. Stanley:
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions. 9940-9951 - Yuguang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan:
Haar Graph Pooling. 9952-9962 - Zhen Wang, Liu Liu, Dacheng Tao:
Deep Streaming Label Learning. 9963-9972 - Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao, Donald K. K. Lee:
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates. 9973-9982 - Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime G. Carbonell, Graham Neubig:
Optimizing Data Usage via Differentiable Rewards. 9983-9995 - Tianyu Wang, Cynthia Rudin:
Bandits for BMO Functions. 9996-10006 - Yaotian Wang, Xiaohang Sun, Jason W. Fleischer:
When deep denoising meets iterative phase retrieval. 10007-10017 - Qi Wang, Herke van Hoof:
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. 10018-10028 - Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei:
Loss Function Search for Face Recognition. 10029-10038 - Junqi Wang, Pei Wang, Patrick Shafto:
Sequential Cooperative Bayesian Inference. 10039-10049 - Yuh-Shyang Wang, Lily Weng, Luca Daniel:
Neural Network Control Policy Verification With Persistent Adversarial Perturbation. 10050-10059 - Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou:
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable. 10060-10069 - Che Wang, Yanqiu Wu, Quan Vuong, Keith W. Ross:
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling. 10070-10080 - Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu:
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. 10081-10091 - Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. 10092-10103 - Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
On Lp-norm Robustness of Ensemble Decision Stumps and Trees. 10104-10114 - Zhendong Wang, Mingyuan Zhou:
Thompson Sampling via Local Uncertainty. 10115-10125 - Peng Wang, Zirui Zhou, Anthony Man-Cho So:
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model. 10126-10135 - Taylor W. Webb, Zachary Dulberg, Steven Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan Cohen:
Learning Representations that Support Extrapolation. 10136-10146 - Feng Wei:
PoKED: A Semi-Supervised System for Word Sense Disambiguation. 10147-10157 - Kaixuan Wei, Angelica I. Avilés-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang:
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems. 10158-10169 - Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain:
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. 10170-10180 - Colin Wei, Sham M. Kakade, Tengyu Ma:
The Implicit and Explicit Regularization Effects of Dropout. 10181-10192 - Asaf Weinstein, Aaditya Ramdas:
Online Control of the False Coverage Rate and False Sign Rate. 10193-10202 - Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans:
Batch Stationary Distribution Estimation. 10203-10213 - Junfeng Wen, Russell Greiner, Dale Schuurmans:
Domain Aggregation Networks for Multi-Source Domain Adaptation. 10214-10224 - Yuxin Wen, Shuai Li, Kui Jia:
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks. 10225-10235 - Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. 10236-10247 - Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? 10248-10259 - Auke J. Wiggers, Emiel Hoogeboom:
Predictive Sampling with Forecasting Autoregressive Models. 10260-10269 - William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin:
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. 10270-10281 - Brian D. Williamson, Jean Feng:
Efficient nonparametric statistical inference on population feature importance using Shapley values. 10282-10291 - James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. 10292-10302 - Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. 10303-10312 - Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka:
Causal Inference using Gaussian Processes with Structured Latent Confounders. 10313-10323 - David P. Woodruff, Amir Zandieh:
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling. 10324-10333 - Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? 10334-10343 - Jingfeng Wu, Vladimir Braverman, Lin Yang:
Obtaining Adjustable Regularization for Free via Iterate Averaging. 10344-10354 - Yinjun Wu, Edgar Dobriban, Susan B. Davidson:
DeltaGrad: Rapid retraining of machine learning models. 10355-10366 - Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu:
On the Noisy Gradient Descent that Generalizes as SGD. 10367-10376 - Kaiwen Wu, Allen Houze Wang, Yaoliang Yu:
Stronger and Faster Wasserstein Adversarial Attacks. 10377-10387 - Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Jian-Huang Lai, Tao Qin, Tie-Yan Liu:
Sequence Generation with Mixed Representations. 10388-10398 - Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu:
Adversarial Robustness via Runtime Masking and Cleansing. 10399-10409 - Sen Wu, Hongyang R. Zhang, Gregory Valiant, Christopher Ré:
On the Generalization Effects of Linear Transformations in Data Augmentation. 10410-10420 - Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent:
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. 10421-10431 - Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang:
Continuous Graph Neural Networks. 10432-10441 - Yunhua Xiang, Noah Simon:
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning. 10442-10451 - Changyi Xiao, Ligang Liu:
Generative Flows with Matrix Exponential. 10452-10461 - Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz:
Disentangling Trainability and Generalization in Deep Neural Networks. 10462-10472 - Yuxuan Xie, Jilles Dibangoye, Olivier Buffet:
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing. 10473-10482 - Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin:
Maximum-and-Concatenation Networks. 10483-10494 - Cong Xie, Sanmi Koyejo, Indranil Gupta:
Zeno++: Robust Fully Asynchronous SGD. 10495-10503 - Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang:
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. 10504-10513 - Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, Ling Shao:
On the Number of Linear Regions of Convolutional Neural Networks. 10514-10523 - Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu:
On Layer Normalization in the Transformer Architecture. 10524-10533 - Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao:
On Variational Learning of Controllable Representations for Text without Supervision. 10534-10543 - Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar:
Class-Weighted Classification: Trade-offs and Robust Approaches. 10544-10554 - Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. 10555-10565 - Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory. 10566-10575 - Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin:
Learning Autoencoders with Relational Regularization. 10576-10586 - Xiangyu Xu, Yongrui Ma, Wenxiu Sun:
Learning Factorized Weight Matrix for Joint Filtering. 10587-10596 - Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng:
Variational Label Enhancement. 10597-10606 - Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik:
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control. 10607-10616 - Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh:
MetaFun: Meta-Learning with Iterative Functional Updates. 10617-10627 - Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell:
Video Prediction via Example Guidance. 10628-10637 - Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams:
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. 10638-10647 - Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger:
Feature Selection using Stochastic Gates. 10648-10659 - Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. 10660-10669 - Yibo Yang, Robert Bamler, Stephan Mandt:
Variational Bayesian Quantization. 10670-10680 - Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. 10681-10692 - Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li:
Randomized Smoothing of All Shapes and Sizes. 10693-10705 - Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, Zhongyu Wei:
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning. 10706-10715 - Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola:
Improving Molecular Design by Stochastic Iterative Target Augmentation. 10716-10726 - Forest Yang, Sanmi Koyejo:
On the consistency of top-k surrogate losses. 10727-10735 - Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi:
Interpolation between Residual and Non-Residual Networks. 10736-10745 - Lin Yang, Mengdi Wang:
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. 10746-10756 - Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang:
Multi-Agent Determinantal Q-Learning. 10757-10766 - Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma:
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. 10767-10777 - Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang:
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks. 10778-10788 - Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok:
Searching to Exploit Memorization Effect in Learning with Noisy Labels. 10789-10798 - Michihiro Yasunaga, Percy Liang:
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback. 10799-10808 - Hui Ye, Zhiyu Chen, Da-Han Wang, Brian D. Davison:
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification. 10809-10819 - Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. 10820-10830 - Gal Yehuda, Moshe Gabel, Assaf Schuster:
It's Not What Machines Can Learn, It's What We Cannot Teach. 10831-10841 - Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. 10842-10851 - Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon:
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning. 10852-10860 - Jaesik Yoon, Gautam Singh, Sungjin Ahn:
Robustifying Sequential Neural Processes. 10861-10870 - Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen:
When Does Self-Supervision Help Graph Convolutional Networks? 10871-10880 - Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie:
Graph Structure of Neural Networks. 10881-10891 - Yang Yu, Shih-Kang Chao, Guang Cheng:
Simultaneous Inference for Massive Data: Distributed Bootstrap. 10892-10901 - Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel:
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach. 10902-10912 - Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao:
Label-Noise Robust Domain Adaptation. 10913-10924 - Xingrui Yu, Yueming Lyu, Ivor W. Tsang:
Intrinsic Reward Driven Imitation Learning via Generative Model. 10925-10935 - Wenhui Yu, Zheng Qin:
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. 10936-10945 - Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. 10946-10956 - Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Training Deep Energy-Based Models with f-Divergence Minimization. 10957-10967 - Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Graph Random Neural Features for Distance-Preserving Graph Representations. 10968-10977 - Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Learning Near Optimal Policies with Low Inherent Bellman Error. 10978-10989 - Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. 10990-11000 - Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew J. Hausknecht:
Learning Calibratable Policies using Programmatic Style-Consistency. 11001-11011 - Junzhe Zhang:
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach. 11012-11022 - Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni:
Robustness to Programmable String Transformations via Augmented Abstract Training. 11023-11032 - Youzhi Zhang, Bo An:
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games. 11033-11043 - Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate. 11044-11054 - Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. 11055-11065 - Hanrui Zhang, Vincent Conitzer:
Learning the Valuations of a k-demand Agent. 11066-11075 - Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Lirong Dai:
A Tree-Structured Decoder for Image-to-Markup Generation. 11076-11085 - Han Zhang, Xi Gao, Jacob Unterman, Tom Arodz:
Approximation Capabilities of Neural ODEs and Invertible Residual Networks. 11086-11095 - Qiuyi (Richard) Zhang, Daniel Golovin:
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. 11096-11105 - Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber:
Spread Divergence. 11106-11116 - Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. 11117-11128 - Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu:
Privately Learning Markov Random Fields. 11129-11140 - Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro:
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective. 11141-11152 - Hang Zhang, Ping Li:
Optimal Estimator for Unlabeled Linear Regression. 11153-11162 - Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian:
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. 11163-11172 - Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie:
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. 11173-11182 - Qiang Zhang, Aldo Lipani, Ömer Kirnap, Emine Yilmaz:
Self-Attentive Hawkes Process. 11183-11193 - Shangtong Zhang, Bo Liu, Shimon Whiteson:
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values. 11194-11203 - Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson:
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. 11204-11213 - Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. 11214-11224 - Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. 11225-11234 - Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page:
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. 11235-11245 - Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal:
Convex Calibrated Surrogates for the Multi-Label F-Measure. 11246-11255 - Brian Hu Zhang, Tuomas Sandholm:
Sparsified Linear Programming for Zero-Sum Equilibrium Finding. 11256-11267 - Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case. 11268-11277 - Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. 11278-11287 - Xuefei Zhang, Songkai Xue, Ji Zhu:
A Flexible Latent Space Model for Multilayer Networks. 11288-11297 - Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull:
Perceptual Generative Autoencoders. 11298-11306 - Jianyi Zhang, Yang Zhao, Changyou Chen:
Variance Reduction in Stochastic Particle-Optimization Sampling. 11307-11316 - Zhenyu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou:
Learning with Feature and Distribution Evolvable Streams. 11317-11327 - Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu:
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. 11328-11339 - Miaoyun Zhao, Yulai Cong, Lawrence Carin:
On Leveraging Pretrained GANs for Generation with Limited Data. 11340-11351 - Han Zhao, Junjie Hu, Andrej Risteski:
On Learning Language-Invariant Representations for Universal Machine Translation. 11352-11364 - Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian:
Do RNN and LSTM have Long Memory? 11365-11375 - Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen:
Feature Quantization Improves GAN Training. 11376-11386 - Shengjia Zhao, Tengyu Ma, Stefano Ermon:
Individual Calibration with Randomized Forecasting. 11387-11397 - Arman Zharmagambetov, Miguel Á. Carreira-Perpiñán:
Smaller, more accurate regression forests using tree alternating optimization. 11398-11408 - Xiantong Zhen, Haoliang Sun, Ying-Jun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek:
Learning to Learn Kernels with Variational Random Features. 11409-11419 - Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie J. Su:
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion. 11420-11435 - Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh:
What Can Learned Intrinsic Rewards Capture? 11436-11446 - Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris N. Metaxas, Chao Chen:
Error-Bounded Correction of Noisy Labels. 11447-11457 - Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang:
Robust Graph Representation Learning via Neural Sparsification. 11458-11468 - Anton Zhiyanov, Alexey Drutsa:
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer. 11469-11480 - Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan:
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting. 11481-11491 - Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with UCB-based Exploration. 11492-11502 - Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi:
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time. 11503-11512 - Yuhao Zhou, Jiaxin Shi, Jun Zhu:
Nonparametric Score Estimators. 11513-11522 - Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes:
Time-Consistent Self-Supervision for Semi-Supervised Learning. 11523-11533 - Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth:
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. 11534-11545 - Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. 11546-11555 - Pan Zhou, Xiao-Tong Yuan:
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization. 11556-11565 - Yinglun Zhu, Sumeet Katariya, Robert D. Nowak:
Robust Outlier Arm Identification. 11566-11575 - Michael Zhu, Chang Liu, Jun Zhu:
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference. 11576-11587 - Liangyu Zhu, Wenbin Lu, Rui Song:
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health. 11588-11598 - Qiuyu Zhu, Vincent Y. F. Tan:
Thompson Sampling Algorithms for Mean-Variance Bandits. 11599-11608 - Sicheng Zhu, Xiao Zhang, David Evans:
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization. 11609-11618 - Daoli Zhu, Lei Zhao:
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming. 11619-11628 - Feng Zhu, Zeyu Zheng:
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment. 11629-11638 - Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James S. Duncan:
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE. 11639-11649 - Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai:
Learning Optimal Tree Models under Beam Search. 11650-11659 - Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed:
Laplacian Regularized Few-Shot Learning. 11660-11670 - Christoph Zimmer, Reza Yaesoubi:
Influenza Forecasting Framework based on Gaussian Processes. 11671-11679 - David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman:
A general recurrent state space framework for modeling neural dynamics during decision-making. 11680-11691 - Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha:
Transformer Hawkes Process. 11692-11702
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