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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, José M. Álvarez, 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 - Zhimeng Pan, Zheng Wang, Jeff M. Phillips, Shandian Zhe:
Self-Adaptable Point Processes with Nonparametric Time Decays. 4594-4606 - Ron Dorfman, Idan Shenfeld, Aviv Tamar:
Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies. 4607-4618 - Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin:
RoMA: Robust Model Adaptation for Offline Model-based Optimization. 4619-4631 - Martin Klissarov, Doina Precup:
Flexible Option Learning. 4632-4646 - Dachao Lin, Ruoyu Sun, Zhihua Zhang:
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. 4647-4660 - Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re:
Online Facility Location with Multiple Advice. 4661-4673 - Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe:
Credit Assignment in Neural Networks through Deep Feedback Control. 4674-4687 - Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou:
Robust Online Correlation Clustering. 4688-4698 - Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. 4699-4711 - Sai Vemprala, Sami Mian, Ashish Kapoor:
Representation Learning for Event-based Visuomotor Policies. 4712-4724 - Arun Kumar Anjanapura Venkatesh, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh:
Kernel Functional Optimisation. 4725-4737 - Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman:
Generalized Shape Metrics on Neural Representations. 4738-4750 - Liang Yang, Mengzhe Li, Liyang Liu, Bingxin Niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo:
Diverse Message Passing for Attribute with Heterophily. 4751-4763 - Mete Kemertas, Tristan Aumentado-Armstrong:
Towards Robust Bisimulation Metric Learning. 4764-4777 - Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka:
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning. 4778-4791 - Ziping Xu, Ambuj Tewari:
Representation Learning Beyond Linear Prediction Functions. 4792-4804 - Lior Yariv, Jiatao Gu, Yoni Kasten, Yaron Lipman:
Volume Rendering of Neural Implicit Surfaces. 4805-4815 - Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaïd Harchaoui:
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. 4816-4828 - Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Sören Pirk, Dominik L. Michels:
Accurately Solving Rod Dynamics with Graph Learning. 4829-4842 - Huy Tuan Pham, Phan-Minh Nguyen:
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training. 4843-4855 - Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi:
Medical Dead-ends and Learning to Identify High-Risk States and Treatments. 4856-4870 - Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham:
Overcoming the Convex Barrier for Simplex Inputs. 4871-4882 - Ashok Cutkosky, Harsh Mehta:
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails. 4883-4895 - Hadi Daneshmand, Amir Joudaki, Francis R. Bach:
Batch Normalization Orthogonalizes Representations in Deep Random Networks. 4896-4906 - Navid Ardeshir, Clayton Sanford, Daniel J. Hsu:
Support vector machines and linear regression coincide with very high-dimensional features. 4907-4918 - Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas M. Breuel, Anima Anandkumar, Jan Kautz:
Coupled Segmentation and Edge Learning via Dynamic Graph Propagation. 4919-4932 - David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. 4933-4946 - Omer Elkabetz, Nadav Cohen:
Continuous vs. Discrete Optimization of Deep Neural Networks. 4947-4960 - Brian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten:
CrypTen: Secure Multi-Party Computation Meets Machine Learning. 4961-4973 - Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra:
Can contrastive learning avoid shortcut solutions? 4974-4986 - Gongwei Chen, Xinhang Song, Bohan Wang, Shuqiang Jiang:
See More for Scene: Pairwise Consistency Learning for Scene Classification. 4987-4999 - Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma:
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. 5000-5011 - Kyra Gan, Su Jia, Andrew A. Li:
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing. 5012-5024 - Peisong Wen, Qianqian Xu, Zhiyong Yang, Yuan He, Qingming Huang:
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking. 5025-5037 - Mohammadreza Armandpour, Ali Sadeghian, Mingyuan Zhou:
Convex Polytope Trees and its Application to VAE. 5038-5051 - Naman Agarwal, Peter Kairouz, Ziyu Liu:
The Skellam Mechanism for Differentially Private Federated Learning. 5052-5064 - Yegor Klochkov, Nikita Zhivotovskiy:
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$. 5065-5076 - Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy J. Mitra, Leonidas J. Guibas, Peter Wonka:
SketchGen: Generating Constrained CAD Sketches. 5077-5088 - Ankit Singh:
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation. 5089-5101 - Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin:
Differentially Private n-gram Extraction. 5102-5111 - Joy Hsu, Jeffrey Gu, Gong Her Wu, Wah Chiu, Serena Yeung:
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations. 5112-5123 - Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Noisy Recurrent Neural Networks. 5124-5137 - Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon, Minah Park, Duwon Park, Youngjune Gwon:
Matrix encoding networks for neural combinatorial optimization. 5138-5149 - Daniella Horan, Eitan Richardson, Yair Weiss:
When Is Unsupervised Disentanglement Possible? 5150-5161 - Ruizhi Deng, Marcus A. Brubaker, Greg Mori, Andreas M. Lehrmann:
Continuous Latent Process Flows. 5162-5173 - Yiheng Lin, Yang Hu, Guanya Shi, Haoyuan Sun, Guannan Qu, Adam Wierman:
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems. 5174-5185 - Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee:
Dataset Distillation with Infinitely Wide Convolutional Networks. 5186-5198 - Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang:
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. 5199-5212 - Zhixing Du, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen:
Distilling Object Detectors with Feature Richness. 5213-5224 - Vanessa Piccolo, Dominik Schröder:
Analysis of one-hidden-layer neural networks via the resolvent method. 5225-5235 - Tristan Karch, Laetitia Teodorescu, Katja Hofmann, Clément Moulin-Frier, Pierre-Yves Oudeyer:
Grounding Spatio-Temporal Language with Transformers. 5236-5249 - Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento:
Learning where to learn: Gradient sparsity in meta and continual learning. 5250-5263 - A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin:
Domain Invariant Representation Learning with Domain Density Transformations. 5264-5275 - Tao Yu, Cuiling Lan, Wenjun Zeng, Mingxiao Feng, Zhizheng Zhang, Zhibo Chen:
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning. 5276-5289 - Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin:
Efficient Equivariant Network. 5290-5302 - Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko:
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. 5303-5315 - Kenneth Borup, Lars Nørvang Andersen:
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation. 5316-5327 - Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. 5328-5344 - Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane:
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines. 5345-5359 - Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon:
Imitation with Neural Density Models. 5360-5372 - Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hernán Curiale, Rubén San José Estépar, Raúl San José Estépar, Marc Niethammer:
Accurate Point Cloud Registration with Robust Optimal Transport. 5373-5389 - Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta:
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. 5390-5401 - Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus:
Automatic Data Augmentation for Generalization in Reinforcement Learning. 5402-5415 - Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia:
Blending Anti-Aliasing into Vision Transformer. 5416-5429 - Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal:
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. 5430-5442 - Yixin Wang, David M. Blei, John P. Cunningham:
Posterior Collapse and Latent Variable Non-identifiability. 5443-5455 - Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. 5456-5468 - Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. 5469-5480 - Thomas Spooner, Nelson Vadori, Sumitra Ganesh:
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs. 5481-5493 - Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin M. Solomon:
MarioNette: Self-Supervised Sprite Learning. 5494-5505 - Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica:
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem. 5506-5517 - Gal Dalal, Assaf Hallak, Steven Dalton, Iuri Frosio, Shie Mannor, Gal Chechik:
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction. 5518-5530 - Subhabrata Dutta, Tanya Gautam, Soumen Chakrabarti, Tanmoy Chakraborty:
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems. 5531-5544 - Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. 5545-5559 - Aounon Kumar, Tom Goldstein:
Center Smoothing: Certified Robustness for Networks with Structured Outputs. 5560-5575 - Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava:
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. 5576-5589 - Colin Conwell, David Mayo, Andrei Barbu, Michael A. Buice, George Alvarez, Boris Katz:
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex. 5590-5607 - Duligur Ibeling, Thomas Icard:
A Topological Perspective on Causal Inference. 5608-5619 - Gregory Szép, Neil Dalchau, Attila Csikász-Nagy:
Parameter Inference with Bifurcation Diagrams. 5620-5630 - Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny:
Scalable Thompson Sampling using Sparse Gaussian Process Models. 5631-5643 - Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang:
Robust Counterfactual Explanations on Graph Neural Networks. 5644-5655 - Adrián Csiszárik, Péter Korösi-Szabó, Ákos K. Matszangosz, Gergely Papp, Dániel Varga:
Similarity and Matching of Neural Network Representations. 5656-5668 - Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida:
DOCTOR: A Simple Method for Detecting Misclassification Errors. 5669-5681 - Hao Zhu, Ke Sun, Peter Koniusz:
Contrastive Laplacian Eigenmaps. 5682-5695 - Kookjin Lee, Nathaniel Trask, Panos Stinis:
Machine learning structure preserving brackets for forecasting irreversible processes. 5696-5707 - Alexander Soen, Ke Sun:
On the Variance of the Fisher Information for Deep Learning. 5708-5719 - Xiu-Shen Wei, Yang Shen, Xuhao Sun, Han-Jia Ye, Jian Yang:
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval. 5720-5730 - Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà:
Shape Registration in the Time of Transformers. 5731-5744 - Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho:
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. 5745-5757 - Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Dissecting the Diffusion Process in Linear Graph Convolutional Networks. 5758-5769 - Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng:
Dynamic Grained Encoder for Vision Transformers. 5770-5783 - Kento Nozawa, Issei Sato:
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. 5784-5797 - Sebastian Damrich, Fred A. Hamprecht:
On UMAP's True Loss Function. 5798-5809 - Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutière:
Fast Pure Exploration via Frank-Wolfe. 5810-5821 - Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li:
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder. 5822-5833 - Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, Ivan Laptev:
History Aware Multimodal Transformer for Vision-and-Language Navigation. 5834-5847 - Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland:
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. 5848-5860 - Irene Solaiman, Christy Dennison:
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets. 5861-5873 - Daron Anderson, Douglas J. Leith:
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains. 5874-5884 - Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti:
Computer-Aided Design as Language. 5885-5897 - Dandan Shan, Richard E. L. Higgins, David F. Fouhey:
COHESIV: Contrastive Object and Hand Embedding Segmentation In Video. 5898-5909 - Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu:
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE. 5910-5920 - Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek Kamilov:
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition. 5921-5933 - Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh:
Group Equivariant Subsampling. 5934-5946 - Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang:
Data Sharing and Compression for Cooperative Networked Control. 5947-5958 - Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon:
Hyperbolic Procrustes Analysis Using Riemannian Geometry. 5959-5971 - Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, Jiashi Feng:
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. 5972-5984 - Jialun Zhang, Salar Fattahi, Richard Y. Zhang:
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization. 5985-5996 - Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang:
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling. 5997-6009 - David R. So, Wojciech Manke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le:
Searching for Efficient Transformers for Language Modeling. 6010-6022 - Max Ryabinin, Andrey Malinin, Mark J. F. Gales:
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets. 6023-6035 - Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang:
Multi-Person 3D Motion Prediction with Multi-Range Transformers. 6036-6049 - Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod K. Varshney:
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. 6050-6061 - Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John M. Pearson:
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds. 6062-6074 - Lirong Xia:
The Semi-Random Satisfaction of Voting Axioms. 6075-6086 - Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, Sanja Fidler:
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis. 6087-6101 - Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Learning to Combine Per-Example Solutions for Neural Program Synthesis. 6102-6114 - Zhengyu Zhao, Zhuoran Liu, Martha A. Larson:
On Success and Simplicity: A Second Look at Transferable Targeted Attacks. 6115-6128 - Guy Blanc, Jane Lange, Li-Yang Tan:
Provably efficient, succinct, and precise explanations. 6129-6141 - Yong Liu:
Refined Learning Bounds for Kernel and Approximate $k$-Means. 6142-6154 - Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu:
Learning Causal Semantic Representation for Out-of-Distribution Prediction. 6155-6170 - Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher:
A first-order primal-dual method with adaptivity to local smoothness. 6171-6182 - Pan Zhou, Caiming Xiong, Xiaotong Yuan, Steven Chu-Hong Hoi:
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning. 6183-6197 - Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro:
Adversarial Robustness with Semi-Infinite Constrained Learning. 6198-6215 - Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar:
Conformal Time-series Forecasting. 6216-6228 - Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng:
A 3D Generative Model for Structure-Based Drug Design. 6229-6239 - Robert Lunde, Purnamrita Sarkar, Rachel A. Ward:
Bootstrapping the Error of Oja's Algorithm. 6240-6252 - Joe Kileel, Timo Klock, João M. Pereira:
Landscape analysis of an improved power method for tensor decomposition. 6253-6265 - Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu:
Curriculum Offline Imitating Learning. 6266-6277 - Dongkai Wang, Shiliang Zhang, Gang Hua:
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference. 6278-6289 - Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis. 6290-6303 - Matteo Sesia, Yaniv Romano:
Conformal Prediction using Conditional Histograms. 6304-6315 - Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong:
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels. 6316-6327 - Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Siheng Chen, Ya Zhang:
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting. 6328-6340 - Rohan Ghosh, Mehul Motani:
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization. 6341-6352 - Aming Wu, Suqi Zhao, Cheng Deng, Wei Liu:
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement. 6353-6364 - Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson:
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. 6365-6379 - Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun:
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay. 6380-6391 - Jiayao Zhang, Hua Wang, Weijie J. Su:
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations. 6392-6403 - Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus:
Probabilistic Forecasting: A Level-Set Approach. 6404-6416 - Miltiadis Kofinas, Naveen Shankar Nagaraja, Efstratios Gavves:
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems. 6417-6429 - Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco:
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. 6430-6441 - Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel:
Scaling Gaussian Processes with Derivative Information Using Variational Inference. 6442-6453 - Ziang Chen, Jianfeng Lu, Yulong Lu:
On the Representation of Solutions to Elliptic PDEs in Barron Spaces. 6454-6465 - Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee:
A/B Testing for Recommender Systems in a Two-sided Marketplace. 6466-6477 - Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt:
Retiring Adult: New Datasets for Fair Machine Learning. 6478-6490 - Paul Liu, Aviad Rubinstein, Jan Vondrák, Junyao Zhao:
Cardinality constrained submodular maximization for random streams. 6491-6502 - Aston Zhang, Yi Tay, Yikang Shen, Alvin Chan, Shuai Zhang:
Self-Instantiated Recurrent Units with Dynamic Soft Recursion. 6503-6514 - Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li:
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. 6515-6528 - Salar Fattahi, Andrés Gómez:
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields. 6529-6541 - Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno:
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation. 6542-6554 - Rishi Saket:
Learnability of Linear Thresholds from Label Proportions. 6555-6566 - Sebastian W. Ober, Laurence Aitchison:
A variational approximate posterior for the deep Wishart process. 6567-6579 - Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster:
Neural Pseudo-Label Optimism for the Bank Loan Problem. 6580-6593 - Mingjie Li, Shaobo Wang, Quanshi Zhang:
Visualizing the Emergence of Intermediate Visual Patterns in DNNs. 6594-6607 - An-Chieh Cheng, Xueting Li, Min Sun, Ming-Hsuan Yang, Sifei Liu:
Learning 3D Dense Correspondence via Canonical Point Autoencoder. 6608-6620 - Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu:
Speech-T: Transducer for Text to Speech and Beyond. 6621-6633 - Wentian Zhao, Xinxiao Wu, Jiebo Luo:
Multi-modal Dependency Tree for Video Captioning. 6634-6645 - Dachao Lin, Haishan Ye, Zhihua Zhang:
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. 6646-6657 - Xiuyuan Cheng, Yao Xie:
Neural Tangent Kernel Maximum Mean Discrepancy. 6658-6670 - Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu-Ming Yiu:
Subgraph Federated Learning with Missing Neighbor Generation. 6671-6682 - Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. 6683-6694 - Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. 6695-6706 - Valerii Likhosherstov, Krzysztof Marcin Choromanski, Jared Quincy Davis, Xingyou Song, Adrian Weller:
Sub-Linear Memory: How to Make Performers SLiM. 6707-6719 - David Friede, Mathias Niepert:
Efficient Learning of Discrete-Continuous Computation Graphs. 6720-6732 - Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. 6733-6746 - Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, Xiao-Ming Wu:
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima. 6747-6761 - Fangzhou Luo, Xiaolin Wu, Yanhui Guo:
Functional Neural Networks for Parametric Image Restoration Problems. 6762-6775 - Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli:
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. 6776-6789 - Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. 6790-6802 - Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. 6803-6815 - Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. 6816-6828 - Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh:
Asynchronous Decentralized SGD with Quantized and Local Updates. 6829-6842 - Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. 6843-6855 - Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. 6856-6867 - Shirli Di-Castro Shashua, Dotan Di Castro, Shie Mannor:
Sim and Real: Better Together. 6868-6880 - Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu:
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions. 6881-6893 - Xinghao Chen, Chang Xu, Minjing Dong, Chunjing Xu, Yunhe Wang:
An Empirical Study of Adder Neural Networks for Object Detection. 6894-6905 - Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? 6906-6919 - Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas:
Teachable Reinforcement Learning via Advice Distillation. 6920-6933 - Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr:
Antipodes of Label Differential Privacy: PATE and ALIBI. 6934-6945 - Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman:
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. 6946-6959 - Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. 6960-6971 - Gregory Dexter, Kevin Bello, Jean Honorio:
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees. 6972-6982 - Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong:
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. 6983-6996 - Sarah Huiyi Cen, Devavrat Shah:
Regulating algorithmic filtering on social media. 6997-7011 - Chengyue Gong, Mao Ye, Qiang Liu:
argmax centroid. 7012-7024 - Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song:
Contrastive Learning of Global and Local Video Representations. 7025-7040 - Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang:
BooVI: Provably Efficient Bootstrapped Value Iteration. 7041-7053 - Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu:
Do Wider Neural Networks Really Help Adversarial Robustness? 7054-7067 - Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. 7068-7081 - Hyuck Lee, Seungjae Shin, Heeyoung Kim:
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning. 7082-7094 - Chris Cundy, Aditya Grover, Stefano Ermon:
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. 7095-7110 - Iulia Duta, Andrei Liviu Nicolicioiu, Marius Leordeanu:
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks. 7111-7125 - Jayadev Acharya, Clément L. Canonne, Prathamesh Mayekar, Himanshu Tyagi:
Information-constrained optimization: can adaptive processing of gradients help? 7126-7138 - Zhengzhuo Xu, Zenghao Chai, Chun Yuan:
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective. 7139-7152 - Daniela Mihai, Jonathon S. Hare:
Learning to Draw: Emergent Communication through Sketching. 7153-7166 - Jesse J. Hagenaars, Federico Paredes-Vallés, Guido de Croon:
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks. 7167-7179 - Bin Dai, Wenliang Li, David P. Wipf:
On the Value of Infinite Gradients in Variational Autoencoder Models. 7180-7192 - Yue Wang, Shaofeng Zou:
Online Robust Reinforcement Learning with Model Uncertainty. 7193-7206 - Angtian Wang, Shenxiao Mei, Alan L. Yuille, Adam Kortylewski:
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose. 7207-7219 - Jiashun Jin, Zheng Tracy Ke, Jiajun Liang:
Sharp Impossibility Results for Hyper-graph Testing. 7220-7231 - Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora:
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. 7232-7241 - Lin Zhao, Huaqing Xiong, Yingbin Liang:
Faster Non-asymptotic Convergence for Double Q-learning. 7242-7253 - Janne H. Korhonen, Dan Alistarh:
Towards Tight Communication Lower Bounds for Distributed Optimisation. 7254-7266 - Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon:
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification. 7267-7280 - Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, Jingdong Wang:
HRFormer: High-Resolution Vision Transformer for Dense Predict. 7281-7293 - Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev:
Manifold Topology Divergence: a Framework for Comparing Data Manifolds. 7294-7305 - Junjie Chen, Li Niu, Liu Liu, Liqing Zhang:
Weak-shot Fine-grained Classification via Similarity Transfer. 7306-7318 - Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper:
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders. 7319-7332 - Blake E. Woodworth, Nathan Srebro:
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. 7333-7345 - Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard:
Indexed Minimum Empirical Divergence for Unimodal Bandits. 7346-7356 - Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra:
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation. 7357-7367 - Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
A Normative and Biologically Plausible Algorithm for Independent Component Analysis. 7368-7384 - Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause:
Regret Bounds for Gaussian-Process Optimization in Large Domains. 7385-7396 - Woochul Kang, Daeyeon Kim:
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks. 7397-7408 - Shinji Ito:
On Optimal Robustness to Adversarial Corruption in Online Decision Problems. 7409-7420 - Neil Gallagher, Kafui Dzirasa, David E. Carlson:
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. 7421-7435 - Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song:
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble. 7436-7447 - Yonghoon Lee, Rina Barber:
Distribution-free inference for regression: discrete, continuous, and in between. 7448-7459 - Kelly W. Zhang, Lucas Janson, Susan A. Murphy:
Statistical Inference with M-Estimators on Adaptively Collected Data. 7460-7471 - Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang:
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem. 7472-7483 - Chonghao Sima, Yexiang Xue:
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning. 7484-7496 - Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn:
Meta-learning with an Adaptive Task Scheduler. 7497-7509 - Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile:
Neural Active Learning with Performance Guarantees. 7510-7521 - Ryo Sato, Mirai Tanaka, Akiko Takeda:
A Gradient Method for Multilevel Optimization. 7522-7533 - Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, Sung Ju Hwang:
Edge Representation Learning with Hypergraphs. 7534-7546 - Akari Asai, Xinyan Yu, Jungo Kasai, Hanna Hajishirzi:
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval. 7547-7560 - Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari:
LEADS: Learning Dynamical Systems that Generalize Across Environments. 7561-7573 - Emile van Krieken, Jakub M. Tomczak, Annette ten Teije:
Storchastic: A Framework for General Stochastic Automatic Differentiation. 7574-7587 - Andreas Maurer, Massimiliano Pontil:
Concentration inequalities under sub-Gaussian and sub-exponential conditions. 7588-7597 - Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. 7598-7610 - Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. 7611-7624 - James Robinson, Mark Herbster:
Improved Regret Bounds for Tracking Experts with Memory. 7625-7636 - Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. 7637-7649 - Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu:
Random Noise Defense Against Query-Based Black-Box Attacks. 7650-7663 - Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao:
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL. 7664-7676 - Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. 7677-7688 - Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang:
Joint Modeling of Visual Objects and Relations for Scene Graph Generation. 7689-7702 - Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. 7703-7717 - Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu:
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization. 7718-7731 - Ilias Diakonikolas, Daniel Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. 7732-7744 - Joseph Pemberton, Ellen Boven, Richard Apps, Rui Ponte Costa:
Cortico-cerebellar networks as decoupling neural interfaces. 7745-7759 - Filippos Kokkinos, Iasonas Kokkinos:
To The Point: Correspondence-driven monocular 3D category reconstruction. 7760-7772 - Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh:
Proper Value Equivalence. 7773-7786 - Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High Dimensional Variational Inference. 7787-7798 - David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. 7799-7812 - Udari Madhushani, Abhimanyu Dubey, Naomi Ehrich Leonard, Alex Pentland:
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication. 7813-7824 - Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman:
Multi-Agent Reinforcement Learning in Stochastic Networked Systems. 7825-7837 - Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey:
Neural Scene Flow Prior. 7838-7851 - Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization. 7852-7864 - Jiayuan Mao, Freda Shi, Jiajun Wu, Roger Levy, Josh Tenenbaum:
Grammar-Based Grounded Lexicon Learning. 7865-7878 - Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry V. Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, Gennady Pekhimenko:
Distributed Deep Learning In Open Collaborations. 7879-7897 - Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. 7898-7911 - Peter Macgregor, He Sun:
Finding Bipartite Components in Hypergraphs. 7912-7923 - Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang:
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation. 7924-7936 - Spencer Frei, Quanquan Gu:
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. 7937-7949 - Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou:
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. 7950-7964 - Linfan Zhang, Arash A. Amini:
Label consistency in overfitted generalized $k$-means. 7965-7977 - Hongxin Wei, Lue Tao, Renchunzi Xie, Bo An:
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise. 7978-7992 - Davin Choo, Tommaso d'Orsi:
The Complexity of Sparse Tensor PCA. 7993-8005 - Cem Anil, Xuchan Bao:
Learning to Elect. 8006-8017 - Pierre Glaser, Michael Arbel, Arthur Gretton:
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. 8018-8031 - Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar:
When Is Generalizable Reinforcement Learning Tractable? 8032-8045 - Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, Minsu Cho:
Relational Self-Attention: What's Missing in Attention for Video Understanding. 8046-8059 - Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan:
Towards Enabling Meta-Learning from Target Models. 8060-8071 - Ibrahim M. Alabdulmohsin, Mario Lucic:
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. 8072-8084 - Martin Engelcke, Oiwi Parker Jones, Ingmar Posner:
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement. 8085-8094 - Boris Hanin, Yi Sun:
How Data Augmentation affects Optimization for Linear Regression. 8095-8105 - Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell:
An Exact Characterization of the Generalization Error for the Gibbs Algorithm. 8106-8118 - Alberto Maria Metelli, Alessio Russo, Marcello Restelli:
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning. 8119-8132 - Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini, Panos Kalnis:
Rethinking gradient sparsification as total error minimization. 8133-8146 - Anindya De, Sanjeev Khanna, Huan Li, MohammadHesam NikpeySalekde:
Approximate optimization of convex functions with outlier noise. 8147-8157 - L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi:
Fair Classification with Adversarial Perturbations. 8158-8171 - Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov:
Distributed Saddle-Point Problems Under Data Similarity. 8172-8184 - Aryan Deshwal, Janardhan Rao Doppa:
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces. 8185-8200 - Hong-You Chen, Wei-Lun Chao:
Gradual Domain Adaptation without Indexed Intermediate Domains. 8201-8214 - Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster:
K-level Reasoning for Zero-Shot Coordination in Hanabi. 8215-8228 - Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris:
Learning Markov State Abstractions for Deep Reinforcement Learning. 8229-8241 - Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger:
Towards Deeper Deep Reinforcement Learning with Spectral Normalization. 8242-8255 - Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui Li:
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery. 8256-8268 - Nimita Shinde, Vishnu Narayanan, James Saunderson:
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering. 8269-8281 - Manuel Dahnert, Ji Hou, Matthias Nießner, Angela Dai:
Panoptic 3D Scene Reconstruction From a Single RGB Image. 8282-8293 - Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka:
Measuring Generalization with Optimal Transport. 8294-8306 - Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Q. Xu:
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering. 8307-8319 - Yilun Du, Katie Collins, Josh Tenenbaum, Vincent Sitzmann:
Learning Signal-Agnostic Manifolds of Neural Fields. 8320-8331 - Richard J. Antonello, Javier S. Turek, Vy Ai Vo, Alexander Huth:
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses. 8332-8344 - Raymond Zhang, Richard Combes:
On the Suboptimality of Thompson Sampling in High Dimensions. 8345-8354 - Sanghyeok Chu, Dongwan Kim, Bohyung Han:
Learning Debiased and Disentangled Representations for Semantic Segmentation. 8355-8366 - Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee:
Diversity Matters When Learning From Ensembles. 8367-8377 - Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models. 8378-8391 - Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya:
Personalized Federated Learning With Gaussian Processes. 8392-8406 - Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. 8407-8418 - Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Implicit SVD for Graph Representation Learning. 8419-8431