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ORCID34th NeurIPS 2021
- Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan:
Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. 2021 - Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. 1-12 - Ahmed Touati, Yann Ollivier:
Learning One Representation to Optimize All Rewards. 13-23 - Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy:
Matrix factorisation and the interpretation of geodesic distance. 24-38 - Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Rajiv Jain, Nikolaos Barmpalios, Ani Nenkova, Tong Sun:
UniDoc: Unified Pretraining Framework for Document Understanding. 39-50 - Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan:
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution. 51-61 - Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh:
Counterfactual Explanations Can Be Manipulated. 62-75 - Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu:
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. 76-89 - Zhao Tang Luo, Huiyan Sang, Bani K. Mallick:
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain. 90-102 - Mina Ghadimi Atigh, Martin Keller-Ressel, Pascal Mettes:
Hyperbolic Busemann Learning with Ideal Prototypes. 103-115 - Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. 116-128 - Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger:
Truncated Marginal Neural Ratio Estimation. 129-143 - Yiyou Sun, Chuan Guo, Yixuan Li:
ReAct: Out-of-distribution Detection With Rectified Activations. 144-157 - Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, Venkatesh Babu R.:
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation. 158-171 - Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzstein:
Fast Training of Neural Lumigraph Representations using Meta Learning. 172-186 - Stefano Sarao Mannelli, Pierfrancesco Urbani:
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems. 187-199 - Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill:
Multimodal Few-Shot Learning with Frozen Language Models. 200-212 - Juha Harviainen, Antti Röyskö
, Mikko Koivisto:
Approximating the Permanent with Deep Rejection Sampling. 213-224 - Yamini Bansal, Preetum Nakkiran, Boaz Barak:
Revisiting Model Stitching to Compare Neural Representations. 225-236 - Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang:
AugMax: Adversarial Composition of Random Augmentations for Robust Training. 237-250 - Andrew Szot, Alexander Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John M. Turner, Noah Maestre, Mustafa Mukadam, Devendra Singh Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel X. Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, Dhruv Batra:
Habitat 2.0: Training Home Assistants to Rearrange their Habitat. 251-266 - Seohong Park, Jaekyeom Kim, Gunhee Kim:
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. 267-279 - Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause:
Meta-Learning Reliable Priors in the Function Space. 280-293 - Sang-Hoon Lee, Ji-Hoon Kim, Hyunseung Chung, Seong-Whan Lee:
VoiceMixer: Adversarial Voice Style Mixup. 294-308 - Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. 309-323 - Guy Kornowski, Ohad Shamir:
Oracle Complexity in Nonsmooth Nonconvex Optimization. 324-334 - Tao Sheng, Jie Chen, Zhouhui Lian:
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection. 335-346 - Ping Zhang, Rishabh K. Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De:
Learning to Select Exogenous Events for Marked Temporal Point Process. 347-361 - Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher:
DRIVE: One-bit Distributed Mean Estimation. 362-377 - Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. 378-391 - Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li:
Progressive Feature Interaction Search for Deep Sparse Network. 392-403 - Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang:
Local Explanation of Dialogue Response Generation. 404-416 - Arno Solin, Ella Tamir, Prakhar Verma:
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation. 417-429 - Robert Ganian, Viktoriia Korchemna:
The Complexity of Bayesian Network Learning: Revisiting the Superstructure. 430-442 - Kazu Ghalamkari, Mahito Sugiyama:
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation. 443-454 - Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. 455-467 - David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels:
Numerical influence of ReLU'(0) on backpropagation. 468-479 - Jyoti Aneja, Alexander G. Schwing, Jan Kautz, Arash Vahdat:
A Contrastive Learning Approach for Training Variational Autoencoder Priors. 480-493 - Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. 494-508 - Zhongzheng Ren, Xiaoming Zhao, Alexander G. Schwing:
Class-agnostic Reconstruction of Dynamic Objects from Videos. 509-522 - Dian Jin, Xin Bing, Yuqian Zhang:
Unique sparse decomposition of low rank matrices. 523-535 - Yonghyeon Lee, Hyeokjun Kwon, Frank C. Park:
Neighborhood Reconstructing Autoencoders. 536-546 - Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou:
TopicNet: Semantic Graph-Guided Topic Discovery. 547-559 - Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. 560-571 - Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré:
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers. 572-585 - Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer G. Dy:
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness. 586-597 - Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang:
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs. 598-609 - Rohan R. Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew C. Gombolay:
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming. 610-623 - Konrad Czechowski, Tomasz Odrzygózdz, Marek Zbysinski, Michal Zawalski, Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski, Piotr Milos:
Subgoal Search For Complex Reasoning Tasks. 624-638 - Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. 639-651 - Keji He, Yan Huang, Qi Wu, Jianhua Yang, Dong An, Shuanglin Sima, Liang Wang:
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision. 652-663 - James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. 664-676 - Rui Huang, Andrew Geng, Yixuan Li:
On the Importance of Gradients for Detecting Distributional Shifts in the Wild. 677-689 - Terrance Liu, Giuseppe Vietri, Steven Wu:
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. 690-702 - Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. 703-714 - Qijia Jiang:
Mirror Langevin Monte Carlo: the Case Under Isoperimetry. 715-725 - Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto:
Do Different Tracking Tasks Require Different Appearance Models? 726-738 - Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George H. Denfield, Andreas S. Tolias, Fabian H. Sinz:
Towards robust vision by multi-task learning on monkey visual cortex. 739-751 - Ryan R. Strauss, Junier B. Oliva:
Arbitrary Conditional Distributions with Energy. 752-763 - Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba:
Learning Domain Invariant Representations in Goal-conditioned Block MDPs. 764-776 - Scott Sussex, Caroline Uhler, Andreas Krause:
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning. 777-788 - Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal:
Fuzzy Clustering with Similarity Queries. 789-801 - Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. 802-814 - Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh:
Sample Selection for Fair and Robust Training. 815-827 - Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai:
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL. 828-839 - Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon:
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. 840-851 - Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila:
Alias-Free Generative Adversarial Networks. 852-863 - Kwanyoung Kim, Jong Chul Ye:
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images. 864-874 - Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang:
Continuous Mean-Covariance Bandits. 875-886 - Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, Chuang Gan:
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language. 887-899 - Ruizhe Qin, Mengying Li, Hu Ding:
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter. 900-913 - Aurick Zhou, Sergey Levine:
Bayesian Adaptation for Covariate Shift. 914-927 - Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George:
Perturb-and-max-product: Sampling and learning in discrete energy-based models. 928-940 - Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu, Yaodong Yang:
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games. 941-952 - Sungyoon Lee, Woojin Lee, Jinseong Park, Jaewook Lee:
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples. 953-964 - Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. 965-979 - Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou:
Global Filter Networks for Image Classification. 980-993 - Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen:
Catastrophic Data Leakage in Vertical Federated Learning. 994-1006 - Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low:
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. 1007-1021 - Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder:
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers. 1022-1035 - Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann:
Distilling Image Classifiers in Object Detectors. 1036-1047 - Jiaqi Ma, Junwei Deng, Qiaozhu Mei:
Subgroup Generalization and Fairness of Graph Neural Networks. 1048-1061 - Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. 1062-1073 - Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan:
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer. 1074-1085 - Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia, Zhuowen Tu, Stefano Soatto:
Long Short-Term Transformer for Online Action Detection. 1086-1099 - Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. 1100-1110 - Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver:
Self-Consistent Models and Values. 1111-1125 - Takanori Maehara, Hoang NT:
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters. 1126-1141 - Marc Rigter, Bruno Lacerda, Nick Hawes:
Risk-Averse Bayes-Adaptive Reinforcement Learning. 1142-1154 - Yichen Qin, Linhan Yu, Yang Li:
Iterative Connecting Probability Estimation for Networks. 1155-1166 - Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang:
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation. 1167-1178 - Koby Bibas, Meir Feder, Tal Hassner:
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. 1179-1191 - Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu:
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation. 1192-1203 - Amit Attia, Tomer Koren:
Algorithmic Instabilities of Accelerated Gradient Descent. 1204-1214 - Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. 1215-1229 - Sheng Zhang, Zhe Zhang, Siva Theja Maguluri:
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning. 1230-1242 - Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza:
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. 1243-1255 - Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. 1256-1272 - Michael Janner, Qiyang Li, Sergey Levine:
Offline Reinforcement Learning as One Big Sequence Modeling Problem. 1273-1286 - Kate Donahue, Jon M. Kleinberg:
Optimality and Stability in Federated Learning: A Game-theoretic Approach. 1287-1298 - Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou:
Understanding Deflation Process in Over-parametrized Tensor Decomposition. 1299-1311 - Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. 1312-1324 - Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran:
On the Value of Interaction and Function Approximation in Imitation Learning. 1325-1336 - Aliakbar Panahi, Seyran Saeedi, Tom Arodz:
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices. 1337-1350 - Masahiro Kato, Kenichiro McAlinn, Shota Yasui:
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy. 1351-1364 - Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson:
Regularized Softmax Deep Multi-Agent Q-Learning. 1365-1377 - Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry:
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. 1378-1389 - Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau:
Systematic Generalization with Edge Transformers. 1390-1402 - Aljaz Bozic, Pablo R. Palafox, Justus Thies, Angela Dai, Matthias Nießner:
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers. 1403-1414 - Yang Song, Conor Durkan, Iain Murray, Stefano Ermon:
Maximum Likelihood Training of Score-Based Diffusion Models. 1415-1428 - Tian Ye, Simon S. Du:
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. 1429-1439 - Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi:
Adaptive Data Augmentation on Temporal Graphs. 1440-1452 - D. Khuê Lê-Huu, Karteek Alahari:
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond. 1453-1467 - Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun:
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs. 1468-1480 - Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto:
Uniform Sampling over Episode Difficulty. 1481-1493 - Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. 1494-1505 - Allen Nie, Emma Brunskill, Chris Piech:
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. 1506-1518 - Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu:
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. 1519-1529 - Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger:
Differentiable Unsupervised Feature Selection based on a Gated Laplacian. 1530-1542 - Clarice Poon, Gabriel Peyré:
Smooth Bilevel Programming for Sparse Regularization. 1543-1555 - Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt:
Grounding Representation Similarity Through Statistical Testing. 1556-1568 - Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio:
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. 1569-1581 - Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. 1582-1593 - Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. 1594-1609 - Gonzalo Jaimovitch-López, David Castellano Falcón, César Ferri, José Hernández-Orallo:
Think Big, Teach Small: Do Language Models Distil Occam's Razor? 1610-1623 - Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen:
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. 1624-1633 - Jiayang Xu, Aniruddhe Pradhan, Karthik Duraisamy:
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems. 1634-1645 - Guangmo Tong:
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems. 1646-1659 - Isaac Gibbs, Emmanuel J. Candès:
Adaptive Conformal Inference Under Distribution Shift. 1660-1672 - Lassi Meronen, Martin Trapp, Arno Solin:
Periodic Activation Functions Induce Stationarity. 1673-1685 - David Acuna, Jonah Philion, Sanja Fidler:
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation. 1686-1699 - Yu Hao, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang:
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network. 1700-1712 - Leonardo Cotta, Christopher Morris, Bruno Ribeiro:
Reconstruction for Powerful Graph Representations. 1713-1726 - Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. 1727-1738 - Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi:
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning. 1739-1751 - Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang:
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. 1752-1765 - Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han:
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization. 1766-1779 - Xuxi Chen, Tianlong Chen, Zhenyu Zhang, Zhangyang Wang:
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership. 1780-1791 - Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie:
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. 1792-1804 - Ziwei Ji, Justin D. Li, Matus Telgarsky:
Early-stopped neural networks are consistent. 1805-1817 - Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu:
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM. 1818-1830 - Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon:
Reliable Decisions with Threshold Calibration. 1831-1844 - Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski:
End-to-End Weak Supervision. 1845-1857 - Vasu Singla, Songwei Ge, Ronen Basri, David W. Jacobs:
Shift Invariance Can Reduce Adversarial Robustness. 1858-1871 - Grant Schoenebeck, Biaoshuai Tao:
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences. 1872-1883 - Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Replay-Guided Adversarial Environment Design. 1884-1897 - Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. 1898-1911 - Ingmar Schubert, Danny Driess, Ozgur S. Oguz, Marc Toussaint:
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. 1912-1924 - Jinhee Lee, Haeri Kim, Youngkyu Hong, Hye Won Chung:
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks. 1925-1938 - Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou:
Online Multi-Armed Bandits with Adaptive Inference. 1939-1951 - Constantinos Daskalakis, Patroklos Stefanou, Rui Yao, Emmanouil Zampetakis:
Efficient Truncated Linear Regression with Unknown Noise Variance. 1952-1963 - Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, Qichao Zhou:
Breaking the Dilemma of Medical Image-to-image Translation. 1964-1978 - Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup:
Temporally Abstract Partial Models. 1979-1991 - Shengcai Liao, Ling Shao:
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification. 1992-2003 - Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche:
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs. 2004-2017 - Alexander Miserlis Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan L. Boyd-Graber, Philip Resnik:
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence. 2018-2033 - Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, Egor V. Kostylev:
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding. 2034-2045 - Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? 2046-2059 - Shai Feldman, Stephen Bates, Yaniv Romano:
Improving Conditional Coverage via Orthogonal Quantile Regression. 2060-2071 - Liwang Zhu, Qi Bao, Zhongzhi Zhang:
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation. 2072-2084 - Shasha Li, Abhishek Aich, Shitong Zhu, M. Salman Asif, Chengyu Song, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy:
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations. 2085-2096 - Uri Sherman, Tomer Koren, Yishay Mansour:
Optimal Rates for Random Order Online Optimization. 2097-2108 - Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. 2109-2121 - Yifan Chen, Qi Zeng, Heng Ji, Yun Yang:
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method. 2122-2135 - Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, Yongbing Zhang:
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification. 2136-2147 - Erlin Pan, Zhao Kang:
Multi-view Contrastive Graph Clustering. 2148-2159 - Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath:
Inverse-Weighted Survival Games. 2160-2172 - Alec Farid, Anirudha Majumdar:
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability. 2173-2186 - Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. 2187-2200 - Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik:
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots. 2201-2214 - Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit:
On Calibration and Out-of-Domain Generalization. 2215-2227 - Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang:
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. 2228-2240 - Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg:
Circa: Stochastic ReLUs for Private Deep Learning. 2241-2252 - Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reinforcement Learning in Reward-Mixing MDPs. 2253-2264 - Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil:
A Gang of Adversarial Bandits. 2265-2279 - Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. 2280-2291 - Fengzhuo Zhang, Vincent Y. F. Tan:
Robustifying Algorithms of Learning Latent Trees with Vector Variables. 2292-2302 - Zheng Zhan, Liang Zhao:
Representation Learning on Spatial Networks. 2303-2318 - Xuhui Fan, Bin Li, Feng Zhou, Scott A. Sisson:
Continuous-time edge modelling using non-parametric point processes. 2319-2330 - Feng Zhu, Andrew R. Sedler, Harrison A. Grier, Nauman Ahad, Mark A. Davenport, Matthew T. Kaufman, Andrea Giovannucci, Chethan Pandarinath:
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. 2331-2345 - Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han:
Memory-efficient Patch-based Inference for Tiny Deep Learning. 2346-2358 - Yipei Wang, Xiaoqian Wang:
Self-Interpretable Model with Transformation Equivariant Interpretation. 2359-2372 - Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras:
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent. 2373-2386 - Constantin Philippenko, Aymeric Dieuleveut:
Preserved central model for faster bidirectional compression in distributed settings. 2387-2399 - Zhifeng Kong, Kamalika Chaudhuri:
Understanding Instance-based Interpretability of Variational Auto-Encoders. 2400-2412 - Feng Liu, Xiaoming Liu:
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image. 2413-2426 - Yusuke Iwasawa, Yutaka Matsuo:
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization. 2427-2440 - Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, Luke Zettlemoyer:
Luna: Linear Unified Nested Attention. 2441-2453 - Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik:
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias. 2454-2465 - Charles Packer, Pieter Abbeel, Joseph E. Gonzalez:
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL. 2466-2477 - Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton:
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. 2478-2490 - Zongxin Yang, Yunchao Wei, Yi Yang:
Associating Objects with Transformers for Video Object Segmentation. 2491-2502 - Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu:
Automatic Symmetry Discovery with Lie Algebra Convolutional Network. 2503-2515 - Maciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski:
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. 2516-2528 - Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao:
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation. 2529-2542 - Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, Chang Xu:
ReSSL: Relational Self-Supervised Learning with Weak Augmentation. 2543-2555 - Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba:
Learning to See by Looking at Noise. 2556-2569 - Maksim Velikanov, Dmitry Yarotsky:
Explicit loss asymptotics in the gradient descent training of neural networks. 2570-2582 - Yizhuo Li, Miao Hao, Zonglin Di, Nitesh B. Gundavarapu, Xiaolong Wang:
Test-Time Personalization with a Transformer for Human Pose Estimation. 2583-2597 - Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang:
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN. 2598-2610 - Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal, Elias Benussi, Frédéric A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano:
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models. 2611-2624 - Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein:
Weisfeiler and Lehman Go Cellular: CW Networks. 2625-2640 - Tiantian He, Yew Soon Ong, Lu Bai:
Learning Conjoint Attentions for Graph Neural Nets. 2641-2653 - Shinji Ito:
Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits. 2654-2667 - Hongyu Gong, Yun Tang, Juan Miguel Pino, Xian Li:
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling. 2668-2681 - Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe:
Cardinality-Regularized Hawkes-Granger Model. 2682-2694 - Yulun Zhang, Huan Wang, Can Qin, Yun Fu:
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution. 2695-2706 - Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks. 2707-2720 - Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A. Bilmes:
Constrained Robust Submodular Partitioning. 2721-2732 - Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit:
Online Knapsack with Frequency Predictions. 2733-2743 - Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On Component Interactions in Two-Stage Recommender Systems. 2744-2757 - Minsu Kim, Joanna Hong, Yong Man Ro:
Lip to Speech Synthesis with Visual Context Attentional GAN. 2758-2770 - Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang:
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis. 2771-2782 - Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-Hwan Ryu, Min Su Lee, Byoung-Tak Zhang:
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning. 2783-2795 - Jonas Köhler, Andreas Krämer, Frank Noé:
Smooth Normalizing Flows. 2796-2809 - Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang:
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images. 2810-2822 - Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh:
Distributed Principal Component Analysis with Limited Communication. 2823-2834 - Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney:
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. 2835-2847 - Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu:
Confident Anchor-Induced Multi-Source Free Domain Adaptation. 2848-2860 - Benyou Wang, Emanuele Di Buccio, Massimo Melucci:
Word2Fun: Modelling Words as Functions for Diachronic Word Representation. 2861-2872 - Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger:
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. 2873-2886 - Justin T. Chiu, Yuntian Deng, Alexander M. Rush:
Low-Rank Constraints for Fast Inference in Structured Models. 2887-2898 - Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu:
Accumulative Poisoning Attacks on Real-time Data. 2899-2912 - Sanae Amani, Christos Thrampoulidis:
UCB-based Algorithms for Multinomial Logistic Regression Bandits. 2913-2924 - Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis:
Estimating the Long-Term Effects of Novel Treatments. 2925-2935 - Chaoqun Wang, Shaobo Min, Xuejin Chen, Xiaoyan Sun, Houqiang Li:
Dual Progressive Prototype Network for Generalized Zero-Shot Learning. 2936-2948 - Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Basar:
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity. 2949-2964 - Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li:
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators. 2965-2977 - Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
On the Existence of The Adversarial Bayes Classifier. 2978-2990 - Denizalp Goktas, Amy Greenwald:
Convex-Concave Min-Max Stackelberg Games. 2991-3003 - Ilija Bogunovic, Andreas Krause:
Misspecified Gaussian Process Bandit Optimization. 3004-3015 - Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. 3016-3028 - Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu:
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. 3029-3042 - Chunjong Park, Anas Awadalla, Tadayoshi Kohno, Shwetak N. Patel:
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection. 3043-3056 - Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire:
Multiclass Boosting and the Cost of Weak Learning. 3057-3067 - Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener:
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks. 3068-3080 - A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa:
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. 3081-3095 - Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. 3096-3107 - Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin:
3D Pose Transfer with Correspondence Learning and Mesh Refinement. 3108-3120 - Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau:
Framing RNN as a kernel method: A neural ODE approach. 3121-3134 - Jianbo Ouyang, Hui Wu, Min Wang, Wengang Zhou, Houqiang Li:
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking. 3135-3148 - Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover:
Can Information Flows Suggest Targets for Interventions in Neural Circuits? 3149-3162 - Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak:
AutoBalance: Optimized Loss Functions for Imbalanced Data. 3163-3177 - Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela M. Wood, Mihaela van der Schaar:
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes. 3178-3190 - Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. 3191-3204 - Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S. Kankanhalli:
Unsupervised Motion Representation Learning with Capsule Autoencoders. 3205-3217 - Yizhou Zhang, Karishma Sharma, Yan Liu:
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media. 3218-3231 - Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin:
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders. 3232-3243 - Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting Shao, Kun Wang, Lei He:
Exploring Forensic Dental Identification with Deep Learning. 3244-3258 - Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Donglai Wei:
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training. 3259-3270 - Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C. Green:
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks. 3271-3284 - Feihu Zhang, Philip H. S. Torr, René Ranftl, Stephan R. Richter:
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning. 3285-3297 - Tom Hess, Michal Moshkovitz, Sivan Sabato:
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering. 3298-3308 - Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson:
Dangers of Bayesian Model Averaging under Covariate Shift. 3309-3322 - Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets from Bandit Feedback. 3323-3335 - Christoph Hertrich, Amitabh Basu, Marco Di Summa, Martin Skutella:
Towards Lower Bounds on the Depth of ReLU Neural Networks. 3336-3348 - Geoff Pleiss, John P. Cunningham:
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. 3349-3363 - Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Exact marginal prior distributions of finite Bayesian neural networks. 3364-3375 - Zichen Miao, Ze Wang, Xiuyuan Cheng, Qiang Qiu:
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks. 3376-3388 - Navid Naderializadeh, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann, Soheil Kolouri:
Pooling by Sliced-Wasserstein Embedding. 3389-3400 - Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. 3401-3412 - Kuan-Lin Chen, Ching Hua Lee, Harinath Garudadri, Bhaskar D. Rao:
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees. 3413-3424 - Thomas Berrett, Yi Yu:
Locally private online change point detection. 3425-3437 - Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish:
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. 3438-3450 - Francesco D'Angelo, Vincent Fortuin:
Repulsive Deep Ensembles are Bayesian. 3451-3465 - Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic:
BayesIMP: Uncertainty Quantification for Causal Data Fusion. 3466-3477 - Yaoyao Liu, Bernt Schiele, Qianru Sun:
RMM: Reinforced Memory Management for Class-Incremental Learning. 3478-3490 - Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne:
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). 3491-3503 - Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen McAleer, Ying Wen, Jun Wang, Yaodong Yang:
Neural Auto-Curricula in Two-Player Zero-Sum Games. 3504-3517 - Patrick Esser, Robin Rombach, Andreas Blattmann, Björn Ommer:
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. 3518-3532 - Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
From global to local MDI variable importances for random forests and when they are Shapley values. 3533-3543 - Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou:
Adversarial Robustness of Streaming Algorithms through Importance Sampling. 3544-3557 - Anji Liu, Guy Van den Broeck:
Tractable Regularization of Probabilistic Circuits. 3558-3570 - Eric Mintun, Alexander Kirillov, Saining Xie:
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness. 3571-3583 - Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Richard J. Radke:
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data. 3584-3595 - Guoyuan An, Yuchi Huo, Sung Eui Yoon:
Hypergraph Propagation and Community Selection for Objects Retrieval. 3596-3608 - Taiji Suzuki, Atsushi Nitanda:
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. 3609-3621 - Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi:
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. 3622-3634 - Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu:
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data. 3635-3649 - Peter Hase, Harry Xie, Mohit Bansal:
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. 3650-3666 - Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus:
Control Variates for Slate Off-Policy Evaluation. 3667-3679 - Nicklas Hansen, Hao Su, Xiaolong Wang:
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation. 3680-3693 - Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li:
On Effective Scheduling of Model-based Reinforcement Learning. 3694-3705 - Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert, Benjamin A. Dunn, Philipp Berens, David A. Klindt, Thomas Euler:
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience. 3706-3719 - Prithviraj Ammanabrolu, Mark O. Riedl:
Learning Knowledge Graph-based World Models of Textual Environments. 3720-3731 - Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong:
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. 3732-3743 - Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. 3744-3756 - Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang:
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration. 3757-3769 - Amrith Setlur, Oscar Li, Virginia Smith:
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. 3770-3783 - Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, Qi Wu:
Debiased Visual Question Answering from Feature and Sample Perspectives. 3784-3796 - Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang:
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. 3797-3810 - Mingtian Zhang, Andi Zhang, Steven McDonagh:
On the Out-of-distribution Generalization of Probabilistic Image Modelling. 3811-3823 - Qiujiang Jin, Aryan Mokhtari:
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. 3824-3835 - Moshe Eliasof, Eldad Haber, Eran Treister:
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. 3836-3849 - David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause:
Information Directed Reward Learning for Reinforcement Learning. 3850-3862 - Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi:
SSMF: Shifting Seasonal Matrix Factorization. 3863-3873 - Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz:
Associative Memories via Predictive Coding. 3874-3886 - Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh:
Robust and differentially private mean estimation. 3887-3901 - Kenneth Derek, Phillip Isola:
Adaptable Agent Populations via a Generative Model of Policies. 3902-3913 - Linus Hamilton, Ankur Moitra:
A No-go Theorem for Robust Acceleration in the Hyperbolic Plane. 3914-3924 - Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw:
Privately Learning Mixtures of Axis-Aligned Gaussians. 3925-3938 - Idan Kligvasser, Tamar Rott Shaham, Yuval Bahat, Tomer Michaeli:
Deep Self-Dissimilarities as Powerful Visual Fingerprints. 3939-3951 - Ioana Bica, Daniel Jarrett, Mihaela van der Schaar:
Invariant Causal Imitation Learning for Generalizable Policies. 3952-3964 - Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. 3965-3977 - Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, Liqing Zhang:
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity. 3978-3990 - Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang:
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning. 3991-4002 - Liu Leqi, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery:
Rebounding Bandits for Modeling Satiation Effects. 4003-4014 - Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. 4015-4027 - Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon:
IQ-Learn: Inverse soft-Q Learning for Imitation. 4028-4039 - Dongmin Park, Hwanjun Song, Minseok Kim, Jae-Gil Lee:
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. 4040-4052 - Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth ERM and SCO in Subquadratic Steps. 4053-4064 - Ming Yin, Yu-Xiang Wang:
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. 4065-4078 - Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal:
Speedy Performance Estimation for Neural Architecture Search. 4079-4092 - Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner:
How Tight Can PAC-Bayes be in the Small Data Regime? 4093-4105 - Gregory Clark:
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess. 4106-4119 - Shoutik Mukherjee, Behtash Babadi:
Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit. 4120-4133 - Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar:
Efficient Training of Retrieval Models using Negative Cache. 4134-4146 - Xiuwen Gong, Dong Yuan, Wei Bao:
Understanding Partial Multi-Label Learning via Mutual Information. 4147-4156 - Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust:
Environment Generation for Zero-Shot Compositional Reinforcement Learning. 4157-4169 - Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Optimizing Conditional Value-At-Risk of Black-Box Functions. 4170-4180 - Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling:
E(n) Equivariant Normalizing Flows. 4181-4192 - Chongjian Ge, Youwei Liang, Yibing Song, Jianbo Jiao, Jue Wang, Ping Luo:
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning. 4193-4206 - Severi Rissanen, Pekka Marttinen:
A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. 4207-4217 - Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy A. Mann:
Improving Robustness using Generated Data. 4218-4233 - Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. 4234-4248 - Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen:
Learning to Learn Graph Topologies. 4249-4262 - Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho:
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis. 4263-4273 - Chenning Yu, Sicun Gao:
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks. 4274-4289 - Wenbo Ren, Jia Liu, Ness B. Shroff:
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons. 4290-4300 - Ming Gao, Bryon Aragam:
Efficient Bayesian network structure learning via local Markov boundary search. 4301-4313 - Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim:
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention. 4314-4327 - Sicheng Zhu, Bang An, Furong Huang:
Understanding the Generalization Benefit of Model Invariance from a Data Perspective. 4328-4341 - Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du:
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP. 4342-4355 - Xinshuai Dong, Anh Tuan Luu, Min Lin, Shuicheng Yan, Hanwang Zhang:
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? 4356-4369 - Robert Lieck, Martin Rohrmeier:
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks. 4370-4383 - Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin:
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. 4384-4396 - Kamélia Daudel, Randal Douc:
Mixture weights optimisation for Alpha-Divergence Variational Inference. 4397-4408 - Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. 4409-4420 - Gavin Kerrigan, Padhraic Smyth, Mark Steyvers:
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. 4421-4434 - Debjit Paria, Abhishek Sinha:
$\texttt{LeadCache}$: Regret-Optimal Caching in Networks. 4435-4447 - Prasad Gabbur, Manjot Bilkhu, Javier R. Movellan:
Probabilistic Attention for Interactive Segmentation. 4448-4460 - Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta:
Influence Patterns for Explaining Information Flow in BERT. 4461-4474 - Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian:
Robust Regression Revisited: Acceleration and Improved Estimation Rates. 4475-4488 - Yue Zhao, Ryan A. Rossi, Leman Akoglu:
Automatic Unsupervised Outlier Model Selection. 4489-4502 - Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue:
Pruning Randomly Initialized Neural Networks with Iterative Randomization. 4503-4513 - Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo:
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training. 4514-4528 - Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang:
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. 4529-4541 - Xiang Zhou, Yi Xiong, Ningyuan Chen, Xuefeng Gao:
Regime Switching Bandits. 4542-4554 - Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li:
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps. 4555-4569 - Suhas Vijaykumar:
Localization, Convexity, and Star Aggregation. 4570-4581 - Mugalodi Rakesh, Jogendra Nath Kundu, Varun Jampani, Venkatesh Babu R.:
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery. 4582-4593