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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 Turner, Noah Maestre, Mustafa Mukadam, Devendra Singh Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel X. Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, Dhruv Batra:
Habitat 2.0: Training Home Assistants to Rearrange their Habitat. 251-266 - Seohong Park, Jaekyeom Kim, Gunhee Kim:
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. 267-279 - Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause:
Meta-Learning Reliable Priors in the Function Space. 280-293 - Sang-Hoon Lee, Ji-Hoon Kim, Hyunseung Chung, Seong-Whan Lee:
VoiceMixer: Adversarial Voice Style Mixup. 294-308 - Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. 309-323 - Guy Kornowski, Ohad Shamir:
Oracle Complexity in Nonsmooth Nonconvex Optimization. 324-334 - Tao Sheng, Jie Chen, Zhouhui Lian:
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection. 335-346 - Ping Zhang, Rishabh K. Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De:
Learning to Select Exogenous Events for Marked Temporal Point Process. 347-361 - Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher:
DRIVE: One-bit Distributed Mean Estimation. 362-377 - Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. 378-391 - Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li:
Progressive Feature Interaction Search for Deep Sparse Network. 392-403 - Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang:
Local Explanation of Dialogue Response Generation. 404-416 - Arno Solin, Ella Tamir, Prakhar Verma:
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation. 417-429 - Robert Ganian, Viktoriia Korchemna:
The Complexity of Bayesian Network Learning: Revisiting the Superstructure. 430-442 - Kazu Ghalamkari, Mahito Sugiyama:
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation. 443-454 - Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. 455-467 - David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels:
Numerical influence of ReLU'(0) on backpropagation. 468-479 - Jyoti Aneja, Alexander G. Schwing, Jan Kautz, Arash Vahdat:
A Contrastive Learning Approach for Training Variational Autoencoder Priors. 480-493 - Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. 494-508 - Zhongzheng Ren, Xiaoming Zhao, Alexander G. Schwing:
Class-agnostic Reconstruction of Dynamic Objects from Videos. 509-522 - Dian Jin, Xin Bing, Yuqian Zhang:
Unique sparse decomposition of low rank matrices. 523-535 - Yonghyeon Lee, Hyeokjun Kwon, Frank C. Park:
Neighborhood Reconstructing Autoencoders. 536-546 - Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou:
TopicNet: Semantic Graph-Guided Topic Discovery. 547-559 - Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. 560-571 - Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré:
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers. 572-585 - Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer G. Dy:
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness. 586-597 - Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang:
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs. 598-609 - Rohan R. Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew C. Gombolay:
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming. 610-623 - Konrad Czechowski, Tomasz Odrzygózdz, Marek Zbysinski, Michal Zawalski, Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski, Piotr Milos:
Subgoal Search For Complex Reasoning Tasks. 624-638 - Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. 639-651 - Keji He, Yan Huang, Qi Wu, Jianhua Yang, Dong An, Shuanglin Sima, Liang Wang:
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision. 652-663 - James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. 664-676 - Rui Huang, Andrew Geng, Yixuan Li:
On the Importance of Gradients for Detecting Distributional Shifts in the Wild. 677-689 - Terrance Liu, Giuseppe Vietri, Steven Wu
:
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. 690-702 - Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. 703-714 - Qijia Jiang:
Mirror Langevin Monte Carlo: the Case Under Isoperimetry. 715-725 - Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto:
Do Different Tracking Tasks Require Different Appearance Models? 726-738 - Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George H. Denfield, Andreas S. Tolias, Fabian H. Sinz:
Towards robust vision by multi-task learning on monkey visual cortex. 739-751 - Ryan R. Strauss, Junier B. Oliva:
Arbitrary Conditional Distributions with Energy. 752-763 - Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba:
Learning Domain Invariant Representations in Goal-conditioned Block MDPs. 764-776 - Scott Sussex, Caroline Uhler, Andreas Krause:
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning. 777-788 - Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal:
Fuzzy Clustering with Similarity Queries. 789-801 - Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. 802-814 - Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh:
Sample Selection for Fair and Robust Training. 815-827 - Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai:
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL. 828-839 - Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon:
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. 840-851 - Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila:
Alias-Free Generative Adversarial Networks. 852-863 - Kwanyoung Kim, Jong Chul Ye:
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images. 864-874 - Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang:
Continuous Mean-Covariance Bandits. 875-886 - Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, Chuang Gan:
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language. 887-899 - Ruizhe Qin, Mengying Li, Hu Ding:
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter. 900-913 - Aurick Zhou, Sergey Levine:
Bayesian Adaptation for Covariate Shift. 914-927 - Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George:
Perturb-and-max-product: Sampling and learning in discrete energy-based models. 928-940 - Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu, Yaodong Yang:
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games. 941-952 - Sungyoon Lee, Woojin Lee, Jinseong Park, Jaewook Lee:
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples. 953-964 - Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. 965-979 - Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou:
Global Filter Networks for Image Classification. 980-993 - Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen:
Catastrophic Data Leakage in Vertical Federated Learning. 994-1006 - Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low:
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. 1007-1021 - Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder:
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers. 1022-1035 - Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann:
Distilling Image Classifiers in Object Detectors. 1036-1047 - Jiaqi Ma, Junwei Deng, Qiaozhu Mei:
Subgroup Generalization and Fairness of Graph Neural Networks. 1048-1061 - Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. 1062-1073 - Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan:
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer. 1074-1085 - Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia, Zhuowen Tu, Stefano Soatto:
Long Short-Term Transformer for Online Action Detection. 1086-1099 - Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. 1100-1110 - Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver:
Self-Consistent Models and Values. 1111-1125 - Takanori Maehara, Hoang NT:
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters. 1126-1141 - Marc Rigter, Bruno Lacerda, Nick Hawes:
Risk-Averse Bayes-Adaptive Reinforcement Learning. 1142-1154 - Yichen Qin, Linhan Yu, Yang Li:
Iterative Connecting Probability Estimation for Networks. 1155-1166 - Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang:
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation. 1167-1178 - Koby Bibas, Meir Feder, Tal Hassner:
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. 1179-1191 - Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu:
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation. 1192-1203 - Amit Attia, Tomer Koren:
Algorithmic Instabilities of Accelerated Gradient Descent. 1204-1214 - Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. 1215-1229 - Sheng Zhang, Zhe Zhang, Siva Theja Maguluri:
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning. 1230-1242 - Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza:
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. 1243-1255 - Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. 1256-1272 - Michael Janner, Qiyang Li, Sergey Levine:
Offline Reinforcement Learning as One Big Sequence Modeling Problem. 1273-1286 - Kate Donahue, Jon M. Kleinberg:
Optimality and Stability in Federated Learning: A Game-theoretic Approach. 1287-1298 - Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou:
Understanding Deflation Process in Over-parametrized Tensor Decomposition. 1299-1311 - Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. 1312-1324 - Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran:
On the Value of Interaction and Function Approximation in Imitation Learning. 1325-1336 - Aliakbar Panahi, Seyran Saeedi, Tom Arodz:
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices. 1337-1350 - Masahiro Kato, Kenichiro McAlinn, Shota Yasui:
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy. 1351-1364 - Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson:
Regularized Softmax Deep Multi-Agent Q-Learning. 1365-1377 - Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry:
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. 1378-1389 - Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau:
Systematic Generalization with Edge Transformers. 1390-1402 - Aljaz Bozic, Pablo R. Palafox, Justus Thies, Angela Dai, Matthias Nießner:
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers. 1403-1414 - Yang Song, Conor Durkan, Iain Murray, Stefano Ermon:
Maximum Likelihood Training of Score-Based Diffusion Models. 1415-1428 - Tian Ye, Simon S. Du:
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. 1429-1439 - Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi:
Adaptive Data Augmentation on Temporal Graphs. 1440-1452 - D. Khuê Lê-Huu, Karteek Alahari:
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond. 1453-1467 - Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun:
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs. 1468-1480 - Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto:
Uniform Sampling over Episode Difficulty. 1481-1493 - Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. 1494-1505 - Allen Nie, Emma Brunskill, Chris Piech:
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. 1506-1518 - Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu:
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. 1519-1529 - Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger:
Differentiable Unsupervised Feature Selection based on a Gated Laplacian. 1530-1542 - Clarice Poon, Gabriel Peyré:
Smooth Bilevel Programming for Sparse Regularization. 1543-1555 - Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt:
Grounding Representation Similarity Through Statistical Testing. 1556-1568 - Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio:
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. 1569-1581