| 2021 | $(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations. Zhichun Huang, Shaojie Bai, J. Zico Kolter |
| 2021 | $\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression. Jiabo He, Sarah M. Erfani, Xingjun Ma, James Bailey, Ying Chi, Xian-Sheng Hua |
| 2021 | $\texttt{LeadCache}$: Regret-Optimal Caching in Networks. Debjit Paria, Abhishek Sinha |
| 2021 | (Almost) Free Incentivized Exploration from Decentralized Learning Agents. Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen |
| 2021 | 3D Pose Transfer with Correspondence Learning and Mesh Refinement. Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin |
| 2021 | 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds. Le Hui, Lingpeng Wang, Mingmei Cheng, Jin Xie, Jian Yang |
| 2021 | 3DP3: 3D Scene Perception via Probabilistic Programming. Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka |
| 2021 | A 3D Generative Model for Structure-Based Drug Design. Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng |
| 2021 | A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton |
| 2021 | A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs. Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, Xiaokang Yang |
| 2021 | A Biased Graph Neural Network Sampler with Near-Optimal Regret. Qingru Zhang, David Wipf, Quan Gan, Le Song |
| 2021 | A Causal Lens for Controllable Text Generation. Zhiting Hu, Li Erran Li |
| 2021 | A Central Limit Theorem for Differentially Private Query Answering. Jinshuo Dong, Weijie J. Su, Linjun Zhang |
| 2021 | A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms. Anand Kalvit, Assaf Zeevi |
| 2021 | A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck |
| 2021 | A Comprehensively Tight Analysis of Gradient Descent for PCA. Zhiqiang Xu, Ping Li |
| 2021 | A Computationally Efficient Method for Learning Exponential Family Distributions. Abhin Shah, Devavrat Shah, Gregory W. Wornell |
| 2021 | A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio |
| 2021 | A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering. Tom Hess, Michal Moshkovitz, Sivan Sabato |
| 2021 | A Continuous Mapping For Augmentation Design. Keyu Tian, Chen Lin, Ser-Nam Lim, Wanli Ouyang, Puneet K. Dokania, Philip H. S. Torr |
| 2021 | A Contrastive Learning Approach for Training Variational Autoencoder Priors. Jyoti Aneja, Alexander G. Schwing, Jan Kautz, Arash Vahdat |
| 2021 | A Convergence Analysis of Gradient Descent on Graph Neural Networks. Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi |
| 2021 | A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. Severi Rissanen, Pekka Marttinen |
| 2021 | A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance. Sudeep Salgia, Sattar Vakili, Qing Zhao |
| 2021 | A Faster Decentralized Algorithm for Nonconvex Minimax Problems. Wenhan Xian, Feihu Huang, Yanfu Zhang, Heng Huang |
| 2021 | A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning. Nathaniel Lahn, Sharath Raghvendra, Jiacheng Ye |
| 2021 | A Framework to Learn with Interpretation. Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc |
| 2021 | A Gang of Adversarial Bandits. Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil |
| 2021 | A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning. Weishi Shi, Dayou Yu, Qi Yu |
| 2021 | A Geometric Analysis of Neural Collapse with Unconstrained Features. Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu |
| 2021 | A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition. Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira |
| 2021 | A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast. Jongmin Lee, Chanwoo Park, Ernest K. Ryu |
| 2021 | A Gradient Method for Multilevel Optimization. Ryo Sato, Mirai Tanaka, Akiko Takeda |
| 2021 | A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems. Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Xialiang Tong, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng |
| 2021 | A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression. Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte |
| 2021 | A Kernel-based Test of Independence for Cluster-correlated Data. Hongjiao Liu, Anna M. Plantinga, Yunhua Xiang, Michael C. Wu |
| 2021 | A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning. Gugan Thoppe, Bhumesh Kumar |
| 2021 | A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks. Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon |
| 2021 | A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning. Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng |
| 2021 | A Max-Min Entropy Framework for Reinforcement Learning. Seungyul Han, Youngchul Sung |
| 2021 | A Minimalist Approach to Offline Reinforcement Learning. Scott Fujimoto, Shixiang Shane Gu |
| 2021 | A Multi-Implicit Neural Representation for Fonts. Pradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher, Hailin Jin, Niloy J. Mitra |
| 2021 | A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. Ibrahim M. Alabdulmohsin, Mario Lucic |
| 2021 | A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum. Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang |
| 2021 | A New Theoretical Framework for Fast and Accurate Online Decision-Making. Nicolò Cesa-Bianchi, Tommaso Cesari, Yishay Mansour, Vianney Perchet |
| 2021 | A No-go Theorem for Robust Acceleration in the Hyperbolic Plane. Linus Hamilton, Ankur Moitra |
| 2021 | A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations. Yong Sheng Soh, Antonios Varvitsiotis |
| 2021 | A Normative and Biologically Plausible Algorithm for Independent Component Analysis. Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta |
| 2021 | A Note on Sparse Generalized Eigenvalue Problem. Yunfeng Cai, Guanhua Fang, Ping Li |
| 2021 | A PAC-Bayes Analysis of Adversarial Robustness. Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant |
| 2021 | A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. Jonathan Schmidt, Nicholas Krämer, Philipp Hennig |
| 2021 | A Prototype-Oriented Framework for Unsupervised Domain Adaptation. Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou |
| 2021 | A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert |
| 2021 | A Provably Efficient Sample Collection Strategy for Reinforcement Learning. Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2021 | A Regression Approach to Learning-Augmented Online Algorithms. Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi |
| 2021 | A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks. Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta |
| 2021 | A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis. Xingang Pan, Xudong Xu, Chen Change Loy, Christian Theobalt, Bo Dai |
| 2021 | A Stochastic Newton Algorithm for Distributed Convex Optimization. Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth |
| 2021 | A Surrogate Objective Framework for Prediction+Programming with Soft Constraints. Kai Yan, Jie Yan, Chuan Luo, Liting Chen, Qingwei Lin, Dongmei Zhang |
| 2021 | A Theoretical Analysis of Fine-tuning with Linear Teachers. Gal Shachaf, Alon Brutzkus, Amir Globerson |
| 2021 | A Theory of the Distortion-Perception Tradeoff in Wasserstein Space. Dror Freirich, Tomer Michaeli, Ron Meir |
| 2021 | A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning. Pan Zhou, Caiming Xiong, Xiaotong Yuan, Steven Chu-Hong Hoi |
| 2021 | A Topological Perspective on Causal Inference. Duligur Ibeling, Thomas Icard |
| 2021 | A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal |
| 2021 | A Unified Approach to Fair Online Learning via Blackwell Approachability. Evgenii Chzhen, Christophe Giraud, Gilles Stoltz |
| 2021 | A Unified View of cGANs with and without Classifiers. Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin |
| 2021 | A Universal Law of Robustness via Isoperimetry. Sébastien Bubeck, Mark Sellke |
| 2021 | A Variational Perspective on Diffusion-Based Generative Models and Score Matching. Chin-Wei Huang, Jae Hyun Lim, Aaron C. Courville |
| 2021 | A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura |
| 2021 | A first-order primal-dual method with adaptivity to local smoothness. Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher |
| 2021 | A flow-based latent state generative model of neural population responses to natural images. Mohammad Bashiri, Edgar Y. Walker, Konstantin-Klemens Lurz, Akshay Kumar Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas S. Tolias, Fabian H. Sinz |
| 2021 | A generative nonparametric Bayesian model for whole genomes. Alan Nawzad Amin, Eli N. Weinstein, Debora S. Marks |
| 2021 | A mechanistic multi-area recurrent network model of decision-making. Michael Kleinman, Chandramouli Chandrasekaran, Jonathan C. Kao |
| 2021 | A nonparametric method for gradual change problems with statistical guarantees. Lizhen Nie, Dan Nicolae |
| 2021 | A novel notion of barycenter for probability distributions based on optimal weak mass transport. Elsa Cazelles, Felipe A. Tobar, Joaquín Fontbona |
| 2021 | A sampling-based circuit for optimal decision making. Camille E. Rullán Buxó, Cristina Savin |
| 2021 | A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. Gadi Naveh, Zohar Ringel |
| 2021 | A single gradient step finds adversarial examples on random two-layers neural networks. Sébastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel, Remi Tachet des Combes |
| 2021 | A unified framework for bandit multiple testing. Ziyu Xu, Ruodu Wang, Aaditya Ramdas |
| 2021 | A universal probabilistic spike count model reveals ongoing modulation of neural variability. David Liu, Máté Lengyel |
| 2021 | A variational approximate posterior for the deep Wishart process. Sebastian W. Ober, Laurence Aitchison |
| 2021 | A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval. Xiu-Shen Wei, Yang Shen, Xuhao Sun, Han-Jia Ye, Jian Yang |
| 2021 | A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose. Shih-Yang Su, Frank Yu, Michael Zollhöfer, Helge Rhodin |
| 2021 | A/B Testing for Recommender Systems in a Two-sided Marketplace. Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee |
| 2021 | A/B/n Testing with Control in the Presence of Subpopulations. Yoan Russac, Christina Katsimerou, Dennis Bohle, Olivier Cappé, Aurélien Garivier, Wouter M. Koolen |
| 2021 | ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning. Hyuck Lee, Seungjae Shin, Heeyoung Kim |
| 2021 | AC-GC: Lossy Activation Compression with Guaranteed Convergence. R. David Evans, Tor M. Aamodt |
| 2021 | AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh |
| 2021 | AFEC: Active Forgetting of Negative Transfer in Continual Learning. Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong |
| 2021 | ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning. Guocheng Qian, Hasan Hammoud, Guohao Li, Ali K. Thabet, Bernard Ghanem |
| 2021 | ATISS: Autoregressive Transformers for Indoor Scene Synthesis. Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, Sanja Fidler |
| 2021 | Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships. Lifeng Zhang |
| 2021 | Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, Daniel Soudry |
| 2021 | Accelerating Quadratic Optimization with Reinforcement Learning. Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg |
| 2021 | Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives. Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov |
| 2021 | Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. Jingfeng Wu, Vladimir Braverman, Lin Yang |
| 2021 | Accumulative Poisoning Attacks on Real-time Data. Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu |
| 2021 | Accurate Point Cloud Registration with Robust Optimal Transport. Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hernán Curiale, Rubén San José Estépar, Raúl San José Estépar, Marc Niethammer |
| 2021 | Accurately Solving Rod Dynamics with Graph Learning. Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Sören Pirk, Dominik L. Michels |
| 2021 | Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning. Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, Lei Shu |
| 2021 | Achieving Rotational Invariance with Bessel-Convolutional Neural Networks. Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, Alexandre Mayer |
| 2021 | Across-animal odor decoding by probabilistic manifold alignment. Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin |
| 2021 | Action-guided 3D Human Motion Prediction. Jiangxin Sun, Zihang Lin, Xintong Han, Jian-Fang Hu, Jia Xu, Wei-Shi Zheng |
| 2021 | Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection. Sunghyeon Woo, Jeongwoo Park, Jiwoo Hong, Dongsuk Jeon |
| 2021 | Active 3D Shape Reconstruction from Vision and Touch. Edward J. Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero-Soriano, Michal Drozdzal |
| 2021 | Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations. Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi |
| 2021 | Active Learning of Convex Halfspaces on Graphs. Maximilian Thiessen, Thomas Gärtner |
| 2021 | Active Offline Policy Selection. Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas |
| 2021 | Active clustering for labeling training data. Quentin Lutz, Elie de Panafieu, Maya Stein, Alex Scott |
| 2021 | Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable. Tian-Zuo Wang, Zhi-Hua Zhou |
| 2021 | Adaptable Agent Populations via a Generative Model of Policies. Kenneth Derek, Phillip Isola |
| 2021 | Adapting to function difficulty and growth conditions in private optimization. Hilal Asi, Daniel Levy, John C. Duchi |
| 2021 | Adaptive Conformal Inference Under Distribution Shift. Isaac Gibbs, Emmanuel J. Candès |
| 2021 | Adaptive Data Augmentation on Temporal Graphs. Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi |
| 2021 | Adaptive Denoising via GainTuning. Sreyas Mohan, Joshua L. Vincent, Ramon Manzorro, Peter A. Crozier, Carlos Fernandez-Granda, Eero P. Simoncelli |
| 2021 | Adaptive Diffusion in Graph Neural Networks. Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang |
| 2021 | Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback. Hang Wang, Sen Lin, Junshan Zhang |
| 2021 | Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements. Kimon Antonakopoulos, Panayotis Mertikopoulos |
| 2021 | Adaptive Machine Unlearning. Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites |
| 2021 | Adaptive Online Packing-guided Search for POMDPs. Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, Jianye Hao |
| 2021 | Adaptive Proximal Gradient Methods for Structured Neural Networks. Jihun Yun, Aurélie C. Lozano, Eunho Yang |
| 2021 | Adaptive Risk Minimization: Learning to Adapt to Domain Shift. Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn |
| 2021 | Adaptive Sampling for Minimax Fair Classification. Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi |
| 2021 | Adaptive wavelet distillation from neural networks through interpretations. Wooseok Ha, Chandan Singh, François Lanusse, Srigokul Upadhyayula, Bin Yu |
| 2021 | Adder Attention for Vision Transformer. Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang |
| 2021 | Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning. Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, Fei Wang |
| 2021 | Adjusting for Autocorrelated Errors in Neural Networks for Time Series. Fan-Keng Sun, Christopher I. Lang, Duane S. Boning |
| 2021 | Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan |
| 2021 | Adversarial Attack Generation Empowered by Min-Max Optimization. Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li |
| 2021 | Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations. Shasha Li, Abhishek Aich, Shitong Zhu, M. Salman Asif, Chengyu Song, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy |
| 2021 | Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong |
| 2021 | Adversarial Examples Make Strong Poisons. Liam Fowl, Micah Goldblum, Ping-Yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein |
| 2021 | Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams. Chawin Sitawarin, Evgenios M. Kornaropoulos, Dawn Song, David A. Wagner |
| 2021 | Adversarial Examples in Multi-Layer Random ReLU Networks. Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri |
| 2021 | Adversarial Feature Desensitization. Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake A. Richards, Irina Rish |
| 2021 | Adversarial Graph Augmentation to Improve Graph Contrastive Learning. Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville |
| 2021 | Adversarial Intrinsic Motivation for Reinforcement Learning. Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone |
| 2021 | Adversarial Neuron Pruning Purifies Backdoored Deep Models. Dongxian Wu, Yisen Wang |
| 2021 | Adversarial Regression with Doubly Non-negative Weighting Matrices. Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, Viet Anh Nguyen |
| 2021 | Adversarial Reweighting for Partial Domain Adaptation. Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu |
| 2021 | Adversarial Robustness of Streaming Algorithms through Importance Sampling. Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou |
| 2021 | Adversarial Robustness with Non-uniform Perturbations. Ecenaz Erdemir, Jeffrey Bickford, Luca Melis, Sergül Aydöre |
| 2021 | Adversarial Robustness with Semi-Infinite Constrained Learning. Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro |
| 2021 | Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach. Anindya Sarkar, Anirban Sarkar, Sowrya Gali, Vineeth N. Balasubramanian |
| 2021 | Adversarial Teacher-Student Representation Learning for Domain Generalization. Fu-En Yang, Yuan-Chia Cheng, Zu-Yun Shiau, Yu-Chiang Frank Wang |
| 2021 | Adversarial Training Helps Transfer Learning via Better Representations. Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y. Zou |
| 2021 | Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions. Jiachen Sun, Yulong Cao, Christopher B. Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao |
| 2021 | Adversarially Robust Change Point Detection. Mengchu Li, Yi Yu |
| 2021 | Adversarially robust learning for security-constrained optimal power flow. Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry T. Pileggi, J. Zico Kolter |
| 2021 | Agent Modelling under Partial Observability for Deep Reinforcement Learning. Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht |
| 2021 | Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan |
| 2021 | Algorithmic Instabilities of Accelerated Gradient Descent. Amit Attia, Tomer Koren |
| 2021 | Algorithmic stability and generalization of an unsupervised feature selection algorithm. Xinxing Wu, Qiang Cheng |
| 2021 | Alias-Free Generative Adversarial Networks. Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila |
| 2021 | Align before Fuse: Vision and Language Representation Learning with Momentum Distillation. Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Gotmare, Shafiq R. Joty, Caiming Xiong, Steven Chu-Hong Hoi |
| 2021 | Aligned Structured Sparsity Learning for Efficient Image Super-Resolution. Yulun Zhang, Huan Wang, Can Qin, Yun Fu |
| 2021 | Aligning Pretraining for Detection via Object-Level Contrastive Learning. Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin |
| 2021 | Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery. Mugalodi Rakesh, Jogendra Nath Kundu, Varun Jampani, Venkatesh Babu R. |
| 2021 | Alignment Attention by Matching Key and Query Distributions. Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, Mingyuan Zhou |
| 2021 | All Tokens Matter: Token Labeling for Training Better Vision Transformers. Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng |
| 2021 | Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams |
| 2021 | Amortized Variational Inference for Simple Hierarchical Models. Abhinav Agrawal, Justin Domke |
| 2021 | An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu |
| 2021 | An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning. Cyrus Cousins |
| 2021 | An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints. Xin Liu, Bin Li, Pengyi Shi, Lei Ying |
| 2021 | An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning. Tianpei Yang, Weixun Wang, Hongyao Tang, Jianye Hao, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yingfeng Chen, Yujing Hu, Changjie Fan, Chengwei Zhang |
| 2021 | An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab |
| 2021 | An Empirical Study of Adder Neural Networks for Object Detection. Xinghao Chen, Chang Xu, Minjing Dong, Chunjing Xu, Yunhe Wang |
| 2021 | An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. Blake E. Woodworth, Nathan Srebro |
| 2021 | An Exact Characterization of the Generalization Error for the Gibbs Algorithm. Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell |
| 2021 | An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks. Shashank Rajput, Kartik Sreenivasan, Dimitris S. Papailiopoulos, Amin Karbasi |
| 2021 | An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap. Yuanhao Wang, Ruosong Wang, Sham M. Kakade |
| 2021 | An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild. Aviv Gabbay, Niv Cohen, Yedid Hoshen |
| 2021 | An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders. Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin |
| 2021 | An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. Anastasia Koloskova, Tao Lin, Sebastian U. Stich |
| 2021 | An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2021 | An Information-theoretic Approach to Distribution Shifts. Marco Federici, Ryota Tomioka, Patrick Forré |
| 2021 | An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang |
| 2021 | An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. Zihang Meng, Rudrasis Chakraborty, Vikas Singh |
| 2021 | An Uncertainty Principle is a Price of Privacy-Preserving Microdata. John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson L. Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev |
| 2021 | An analysis of Ermakov-Zolotukhin quadrature using kernels. Ayoub Belhadji |
| 2021 | An online passive-aggressive algorithm for difference-of-squares classification. Lawrence K. Saul |
| 2021 | Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model. Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong Liu |
| 2021 | Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model. Junjie Ma, Ji Xu, Arian Maleki |
| 2021 | Analysis of one-hidden-layer neural networks via the resolvent method. Vanessa Piccolo, Dominik Schröder |
| 2021 | Analytic Insights into Structure and Rank of Neural Network Hessian Maps. Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann |
| 2021 | Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II. Yossi Arjevani, Michael Field |
| 2021 | Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems. Stefano Sarao Mannelli, Pierfrancesco Urbani |
| 2021 | Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation. Souvik Kundu, Qirui Sun, Yao Fu, Massoud Pedram, Peter A. Beerel |
| 2021 | Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels. Hao Wang, Yizhe Huang, Rui Gao, Flávio P. Calmon |
| 2021 | Answering Complex Causal Queries With the Maximum Causal Set Effect. Zachary Markovich |
| 2021 | Anti-Backdoor Learning: Training Clean Models on Poisoned Data. Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma |
| 2021 | Antipodes of Label Differential Privacy: PATE and ALIBI. Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr |
| 2021 | Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. Nate Veldt, Austin R. Benson, Jon M. Kleinberg |
| 2021 | Approximate optimization of convex functions with outlier noise. Anindya De, Sanjeev Khanna, Huan Li, MohammadHesam NikpeySalekde |
| 2021 | Approximating the Permanent with Deep Rejection Sampling. Juha Harviainen, Antti Röyskö, Mikko Koivisto |
| 2021 | Arbitrary Conditional Distributions with Energy. Ryan R. Strauss, Junier B. Oliva |
| 2021 | Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah |
| 2021 | Are Transformers more robust than CNNs? Yutong Bai, Jieru Mei, Alan L. Yuille, Cihang Xie |
| 2021 | Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling |
| 2021 | Artistic Style Transfer with Internal-external Learning and Contrastive Learning. Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu |
| 2021 | Assessing Fairness in the Presence of Missing Data. Yiliang Zhang, Qi Long |
| 2021 | Associating Objects with Transformers for Video Object Segmentation. Zongxin Yang, Yunchao Wei, Yi Yang |
| 2021 | Associative Memories via Predictive Coding. Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz |
| 2021 | Asymptotically Best Causal Effect Identification with Multi-Armed Bandits. Alan Malek, Silvia Chiappa |
| 2021 | Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models. Kohei Miyaguchi |
| 2021 | Asymptotics of representation learning in finite Bayesian neural networks. Jacob A. Zavatone-Veth, Abdulkadir Canatar, Benjamin S. Ruben, Cengiz Pehlevan |
| 2021 | Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection. Morgane Austern, Vasilis Syrgkanis |
| 2021 | Asynchronous Decentralized Online Learning. Jiyan Jiang, Wenpeng Zhang, Jinjie Gu, Wenwu Zhu |
| 2021 | Asynchronous Decentralized SGD with Quantized and Local Updates. Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh |
| 2021 | Asynchronous Stochastic Optimization Robust to Arbitrary Delays. Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain |
| 2021 | Attention Approximates Sparse Distributed Memory. Trenton Bricken, Cengiz Pehlevan |
| 2021 | Attention Bottlenecks for Multimodal Fusion. Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun |
| 2021 | Attention over Learned Object Embeddings Enables Complex Visual Reasoning. David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt M. Botvinick |
| 2021 | Auditing Black-Box Prediction Models for Data Minimization Compliance. Bashir Rastegarpanah, Krishna P. Gummadi, Mark Crovella |
| 2021 | AugMax: Adversarial Composition of Random Augmentations for Robust Training. Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang |
| 2021 | Augmented Shortcuts for Vision Transformers. Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang |
| 2021 | Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation. Fenglin Liu, Chenyu You, Xian Wu, Shen Ge, Sheng Wang, Xu Sun |
| 2021 | AutoBalance: Optimized Loss Functions for Imbalanced Data. Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak |
| 2021 | AutoGEL: An Automated Graph Neural Network with Explicit Link Information. Zhili Wang, Shimin Di, Lei Chen |
| 2021 | Autobahn: Automorphism-based Graph Neural Nets. Erik H. Thiede, Wenda Zhou, Risi Kondor |
| 2021 | Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long |
| 2021 | Automated Discovery of Adaptive Attacks on Adversarial Defenses. Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin T. Vechev |
| 2021 | Automated Dynamic Mechanism Design. Hanrui Zhang, Vincent Conitzer |
| 2021 | Automatic Data Augmentation for Generalization in Reinforcement Learning. Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus |
| 2021 | Automatic Symmetry Discovery with Lie Algebra Convolutional Network. Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu |
| 2021 | Automatic Unsupervised Outlier Model Selection. Yue Zhao, Ryan A. Rossi, Leman Akoglu |
| 2021 | Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach. Chengyue Gong, Xingchao Liu, Qiang Liu |
| 2021 | Automorphic Equivalence-aware Graph Neural Network. Fengli Xu, Quanming Yao, Pan Hui, Yong Li |
| 2021 | Autonomous Reinforcement Learning via Subgoal Curricula. Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn |
| 2021 | Average-Reward Learning and Planning with Options. Yi Wan, Abhishek Naik, Richard S. Sutton |
| 2021 | Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme |
| 2021 | BARTScore: Evaluating Generated Text as Text Generation. Weizhe Yuan, Graham Neubig, Pengfei Liu |
| 2021 | BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain. Zhao Tang Luo, Huiyan Sang, Bani K. Mallick |
| 2021 | BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. Chris Cundy, Aditya Grover, Stefano Ermon |
| 2021 | BCORLE(λ): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market. Yang Zhang, Bo Tang, Qingyu Yang, Dou An, Hongyin Tang, Chenyang Xi, Xueying Li, Feiyu Xiong |
| 2021 | BNS: Building Network Structures Dynamically for Continual Learning. Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, Bing Liu |
| 2021 | Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. Kanishk Gandhi, Gala Stojnic, Brenden M. Lake, Moira R. Dillon |
| 2021 | Backdoor Attack with Imperceptible Input and Latent Modification. Khoa D. Doan, Yingjie Lao, Ping Li |
| 2021 | Backward-Compatible Prediction Updates: A Probabilistic Approach. Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler |
| 2021 | Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion. Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, Dahua Lin |
| 2021 | Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval. Omar Khattab, Christopher Potts, Matei A. Zaharia |
| 2021 | Bandit Learning with Delayed Impact of Actions. Wei Tang, Chien-Ju Ho, Yang Liu |
| 2021 | Bandit Phase Retrieval. Tor Lattimore, Botao Hao |
| 2021 | Bandit Quickest Changepoint Detection. Aditya Gopalan, Braghadeesh Lakshminarayanan, Venkatesh Saligrama |
| 2021 | Bandits with Knapsacks beyond the Worst Case. Karthik Abinav Sankararaman, Aleksandrs Slivkins |
| 2021 | Bandits with many optimal arms. Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier |
| 2021 | Batch Active Learning at Scale. Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar |
| 2021 | Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. Shibo Li, Robert M. Kirby, Shandian Zhe |
| 2021 | Batch Normalization Orthogonalizes Representations in Deep Random Networks. Hadi Daneshmand, Amir Joudaki, Francis R. Bach |
| 2021 | BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer. Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan |
| 2021 | Batched Thompson Sampling. Cem Kalkanli, Ayfer Özgür |
| 2021 | BayesIMP: Uncertainty Quantification for Causal Data Fusion. Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic |
| 2021 | Bayesian Adaptation for Covariate Shift. Aurick Zhou, Sergey Levine |
| 2021 | Bayesian Bellman Operators. Mattie Fellows, Kristian Hartikainen, Shimon Whiteson |
| 2021 | Bayesian Optimization of Function Networks. Raul Astudillo, Peter I. Frazier |
| 2021 | Bayesian Optimization with High-Dimensional Outputs. Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy |
| 2021 | Bayesian decision-making under misspecified priors with applications to meta-learning. Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire |
| 2021 | Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration. Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang |
| 2021 | Behavior From the Void: Unsupervised Active Pre-Training. Hao Liu, Pieter Abbeel |
| 2021 | Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning. Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao |
| 2021 | Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. Chi Jin, Qinghua Liu, Sobhan Miryoosefi |
| 2021 | Bellman-consistent Pessimism for Offline Reinforcement Learning. Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal |
| 2021 | Beltrami Flow and Neural Diffusion on Graphs. Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein |
| 2021 | Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. Ke Wang, Vidya Muthukumar, Christos Thrampoulidis |
| 2021 | BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. Mingguo He, Zhewei Wei, Zengfeng Huang, Hongteng Xu |
| 2021 | Best Arm Identification in Contaminated Stochastic Bandits. Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das |
| 2021 | Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel. Yixin Chen, Tonmoy Dey, Alan Kuhnle |
| 2021 | Best-case lower bounds in online learning. Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi |
| 2021 | Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification. Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter |
| 2021 | Better Algorithms for Individually Fair Maryam Negahbani, Deeparnab Chakrabarty |
| 2021 | Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training. Lue Tao, Lei Feng, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen |
| 2021 | Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game. Alexander G. Reisach, Christof Seiler, Sebastian Weichwald |
| 2021 | Beyond Bandit Feedback in Online Multiclass Classification. Dirk van der Hoeven, Federico Fusco, Nicolò Cesa-Bianchi |
| 2021 | Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning. Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka |
| 2021 | Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider |
| 2021 | Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation. Robert A. Vandermeulen, Antoine Ledent |
| 2021 | Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization. Gaspard Beugnot, Julien Mairal, Alessandro Rudi |
| 2021 | Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert |
| 2021 | Beyond the Signs: Nonparametric Tensor Completion via Sign Series. Chanwoo Lee, Miaoyan Wang |
| 2021 | Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models. Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal, Elias Benussi, Frédéric A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano |
| 2021 | Bias and variance of the Bayesian-mean decoder. Arthur Prat-Carrabin, Michael Woodford |
| 2021 | Biological key-value memory networks. Danil Tyulmankov, Ching Fang, Annapurna Vadaparty, Guangyu Robert Yang |
| 2021 | Black Box Probabilistic Numerics. Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates |
| 2021 | BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation. Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng |
| 2021 | Blending Anti-Aliasing into Vision Transformer. Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia |
| 2021 | BooVAE: Boosting Approach for Continual Learning of VAE. Evgenii Egorov, Anna Kuzina, Evgeny Burnaev |
| 2021 | BooVI: Provably Efficient Bootstrapped Value Iteration. Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2021 | Boost Neural Networks by Checkpoints. Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, Xian Wei, Hairong Lv |
| 2021 | Boosted CVaR Classification. Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar |
| 2021 | Boosting with Multiple Sources. Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh |
| 2021 | Bootstrap Your Object Detector via Mixed Training. Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai |
| 2021 | Bootstrapping the Error of Oja's Algorithm. Robert Lunde, Purnamrita Sarkar, Rachel A. Ward |
| 2021 | Bounds all around: training energy-based models with bidirectional bounds. Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg |
| 2021 | Breaking the Dilemma of Medical Image-to-image Translation. Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, Qichao Zhou |
| 2021 | Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava |
| 2021 | Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. Han Zhong, Jiayi Huang, Lin Yang, Liwei Wang |
| 2021 | Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning. Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi |
| 2021 | Breaking the centralized barrier for cross-device federated learning. Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh |
| 2021 | Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho |
| 2021 | Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators. Qitian Wu, Rui Gao, Hongyuan Zha |
| 2021 | Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection. Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee |
| 2021 | Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell |
| 2021 | Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning. Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut |
| 2021 | Bridging the Imitation Gap by Adaptive Insubordination. Luca Weihs, Unnat Jain, Iou-Jen Liu, Jordi Salvador, Svetlana Lazebnik, Aniruddha Kembhavi, Alexander G. Schwing |
| 2021 | Bubblewrap: Online tiling and real-time flow prediction on neural manifolds. Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John M. Pearson |
| 2021 | BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining. Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh |
| 2021 | ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE. Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu |
| 2021 | CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks. Sakshi Varshney, Vinay Kumar Verma, P. K. Srijith, Lawrence Carin, Piyush Rai |
| 2021 | CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. Zhize Li, Peter Richtárik |
| 2021 | CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings. Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve, Ronan Collobert, Alex Rogozhnikov |
| 2021 | CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator. Alek Dimitriev, Mingyuan Zhou |
| 2021 | CATs: Cost Aggregation Transformers for Visual Correspondence. Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim |
| 2021 | CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method. Guhyun Kim, Doo Seok Jeong |
| 2021 | CCVS: Context-aware Controllable Video Synthesis. Guillaume Le Moing, Jean Ponce, Cordelia Schmid |
| 2021 | CHIP: CHannel Independence-based Pruning for Compact Neural Networks. Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman A. Zonouz, Bo Yuan |
| 2021 | CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation. Ankit Singh |
| 2021 | CLIP-It! Language-Guided Video Summarization. Medhini Narasimhan, Anna Rohrbach, Trevor Darrell |
| 2021 | CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum. Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang |
| 2021 | COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining. Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul Bennett, Jiawei Han, Xia Song |
| 2021 | COHESIV: Contrastive Object and Hand Embedding Segmentation In Video. Dandan Shan, Richard E. L. Higgins, David F. Fouhey |
| 2021 | COMBO: Conservative Offline Model-Based Policy Optimization. Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn |
| 2021 | CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age. Dani Kiyasseh, Tingting Zhu, David A. Clifton |
| 2021 | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon |
| 2021 | Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon |
| 2021 | Calibration and Consistency of Adversarial Surrogate Losses. Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2021 | Can Information Flows Suggest Targets for Interventions in Neural Circuits? Praveen Venkatesh, Sanghamitra Dutta, Neil Ashim Mehta, Pulkit Grover |
| 2021 | Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial. Yang Liu, Jialu Wang |
| 2021 | Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein |
| 2021 | Can contrastive learning avoid shortcut solutions? Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra |
| 2021 | Can fMRI reveal the representation of syntactic structure in the brain? Aniketh Janardhan Reddy, Leila Wehbe |
| 2021 | Can multi-label classification networks know what they don't know? Haoran Wang, Weitang Liu, Alex Bocchieri, Yixuan Li |
| 2021 | Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick |
| 2021 | Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks. Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata, Vineeth N. Balasubramanian |
| 2021 | Canonical Capsules: Self-Supervised Capsules in Canonical Pose. Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi |
| 2021 | Capacity and Bias of Learned Geometric Embeddings for Directed Graphs. Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L. Clarkson, Andrew McCallum |
| 2021 | Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations. Joy Hsu, Jeffrey Gu, Gong Her Wu, Wah Chiu, Serena Yeung |
| 2021 | Cardinality constrained submodular maximization for random streams. Paul Liu, Aviad Rubinstein, Jan Vondrák, Junyao Zhao |
| 2021 | Cardinality-Regularized Hawkes-Granger Model. Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe |
| 2021 | Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication. Lukasz Kucinski, Tomasz Korbak, Pawel Kolodziej, Piotr Milos |
| 2021 | Catastrophic Data Leakage in Vertical Federated Learning. Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen |
| 2021 | Catch-A-Waveform: Learning to Generate Audio from a Single Short Example. Gal Greshler, Tamar Rott Shaham, Tomer Michaeli |
| 2021 | Causal Abstractions of Neural Networks. Atticus Geiger, Hanson Lu, Thomas Icard, Christopher Potts |
| 2021 | Causal Bandits with Unknown Graph Structure. Yangyi Lu, Amirhossein Meisami, Ambuj Tewari |
| 2021 | Causal Effect Inference for Structured Treatments. Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva |
| 2021 | Causal Identification with Matrix Equations. Sanghack Lee, Elias Bareinboim |
| 2021 | Causal Inference for Event Pairs in Multivariate Point Processes. Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Xiao Shou, Nicholas Mattei, Kristin P. Bennett |
| 2021 | Causal Influence Detection for Improving Efficiency in Reinforcement Learning. Maximilian Seitzer, Bernhard Schölkopf, Georg Martius |
| 2021 | Causal Navigation by Continuous-time Neural Networks. Charles Vorbach, Ramin M. Hasani, Alexander Amini, Mathias Lechner, Daniela Rus |
| 2021 | Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal |
| 2021 | Celebrating Diversity in Shared Multi-Agent Reinforcement Learning. Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang |
| 2021 | Center Smoothing: Certified Robustness for Networks with Structured Outputs. Aounon Kumar, Tom Goldstein |
| 2021 | CentripetalText: An Efficient Text Instance Representation for Scene Text Detection. Tao Sheng, Jie Chen, Zhouhui Lian |
| 2021 | Certifying Robustness to Programmable Data Bias in Decision Trees. Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni |
| 2021 | Challenges and Opportunities in High Dimensional Variational Inference. Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari |
| 2021 | Change Point Detection via Multivariate Singular Spectrum Analysis. Arwa Alanqary, Abdullah Omar Alomar, Devavrat Shah |
| 2021 | Channel Permutations for N: M Sparsity. Jeff Pool, Chong Yu |
| 2021 | Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer |
| 2021 | Characterizing possible failure modes in physics-informed neural networks. Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney |
| 2021 | Characterizing the risk of fairwashing. Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara |
| 2021 | Charting and Navigating the Space of Solutions for Recurrent Neural Networks. Elia Turner, Kabir V. Dabholkar, Omri Barak |
| 2021 | Chasing Sparsity in Vision Transformers: An End-to-End Exploration. Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang |
| 2021 | Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin |
| 2021 | Choose a Transformer: Fourier or Galerkin. Shuhao Cao |
| 2021 | Circa: Stochastic ReLUs for Private Deep Learning. Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg |
| 2021 | Class-Disentanglement and Applications in Adversarial Detection and Defense. Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, Dacheng Tao |
| 2021 | Class-Incremental Learning via Dual Augmentation. Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu |
| 2021 | Class-agnostic Reconstruction of Dynamic Objects from Videos. Zhongzheng Ren, Xiaoming Zhao, Alexander G. Schwing |
| 2021 | Clockwork Variational Autoencoders. Vaibhav Saxena, Jimmy Ba, Danijar Hafner |
| 2021 | Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems. Tianyi Chen, Yuejiao Sun, Wotao Yin |
| 2021 | Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation. Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar |
| 2021 | Clustering Effect of Adversarial Robust Models. Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang |
| 2021 | Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning. Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu |
| 2021 | Co-evolution Transformer for Protein Contact Prediction. He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu |
| 2021 | CoAtNet: Marrying Convolution and Attention for All Data Sizes. Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan |
| 2021 | CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration. Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, Slobodan Ilic |
| 2021 | CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney |
| 2021 | Coarse-to-fine Animal Pose and Shape Estimation. Chen Li, Gim Hee Lee |
| 2021 | Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. Frank Schneider, Felix Dangel, Philipp Hennig |
| 2021 | CogView: Mastering Text-to-Image Generation via Transformers. Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang |
| 2021 | Collaborating with Humans without Human Data. DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett |
| 2021 | Collaborative Causal Discovery with Atomic Interventions. Raghavendra Addanki, Shiva Prasad Kasiviswanathan |
| 2021 | Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning). El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê-Nguyên Hoang, Sébastien Rouault |
| 2021 | Collaborative Uncertainty in Multi-Agent Trajectory Forecasting. Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Siheng Chen, Ya Zhang |
| 2021 | Collapsed Variational Bounds for Bayesian Neural Networks. Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner |
| 2021 | Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification. Zhenyu Wang, Ya-Li Li, Ye Guo, Shengjin Wang |
| 2021 | Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach. Ahmed Abbas, Paul Swoboda |
| 2021 | Combinatorial Pure Exploration with Bottleneck Reward Function. Yihan Du, Yuko Kuroki, Wei Chen |
| 2021 | Combiner: Full Attention Transformer with Sparse Computation Cost. Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai |
| 2021 | Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. Gavin Kerrigan, Padhraic Smyth, Mark Steyvers |
| 2021 | Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces. Aryan Deshwal, Janardhan Rao Doppa |
| 2021 | Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers. Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré |
| 2021 | Communication-efficient SGD: From Local SGD to One-Shot Averaging. Artin Spiridonoff, Alex Olshevsky, Yannis Paschalidis |
| 2021 | Compacter: Efficient Low-Rank Hypercomplex Adapter Layers. Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder |
| 2021 | Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie |
| 2021 | Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features. Thomas M. McDonald, Mauricio A. Álvarez |
| 2021 | Compositional Reinforcement Learning from Logical Specifications. Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur |
| 2021 | Compositional Transformers for Scene Generation. Dor Arad Hudson, Larry Zitnick |
| 2021 | Comprehensive Knowledge Distillation with Causal Intervention. Xiang Deng, Zhongfei Zhang |
| 2021 | Compressed Video Contrastive Learning. Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen, Ping Luo |
| 2021 | Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus |
| 2021 | Compressive Visual Representations. Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John F. Canny, Ian Fischer |
| 2021 | Computer-Aided Design as Language. Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti |
| 2021 | ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs. Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu |
| 2021 | Concentration inequalities under sub-Gaussian and sub-exponential conditions. Andreas Maurer, Massimiliano Pontil |
| 2021 | Conditional Generation Using Polynomial Expansions. Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis |
| 2021 | Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems. Jiayang Xu, Aniruddhe Pradhan, Karthik Duraisamy |
| 2021 | Conditioning Sparse Variational Gaussian Processes for Online Decision-making. Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson |
| 2021 | Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality. Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh, Yanan Sui |
| 2021 | Confident Anchor-Induced Multi-Source Free Domain Adaptation. Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu |
| 2021 | Conflict-Averse Gradient Descent for Multi-task learning. Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu |
| 2021 | Conformal Bayesian Computation. Edwin Fong, Chris C. Holmes |
| 2021 | Conformal Prediction using Conditional Histograms. Matteo Sesia, Yaniv Romano |
| 2021 | Conformal Time-series Forecasting. Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar |
| 2021 | Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving. Julien Grand-Clément, Christian Kroer |
| 2021 | Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn |
| 2021 | Conservative Offline Distributional Reinforcement Learning. Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani |
| 2021 | Consistency Regularization for Variational Auto-Encoders. Samarth Sinha, Adji Bousso Dieng |
| 2021 | Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers. Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel |
| 2021 | Consistent Non-Parametric Methods for Maximizing Robustness. Robi Bhattacharjee, Kamalika Chaudhuri |
| 2021 | Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes. Sara Sangalli, Ertunc Erdil, Andreas M. Hötker, Olivio Donati, Ender Konukoglu |
| 2021 | Constrained Robust Submodular Partitioning. Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A. Bilmes |
| 2021 | Container: Context Aggregation Networks. Peng Gao, Jiasen Lu, Hongsheng Li, Roozbeh Mottaghi, Aniruddha Kembhavi |
| 2021 | Contextual Recommendations and Low-Regret Cutting-Plane Algorithms. Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider |
| 2021 | Contextual Similarity Aggregation with Self-attention for Visual Re-ranking. Jianbo Ouyang, Hui Wu, Min Wang, Wengang Zhou, Houqiang Li |
| 2021 | Continual Auxiliary Task Learning. Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White |
| 2021 | Continual Learning via Local Module Composition. Oleksiy Ostapenko, Pau Rodríguez, Massimo Caccia, Laurent Charlin |
| 2021 | Continual World: A Robotic Benchmark For Continual Reinforcement Learning. Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos |
| 2021 | Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien B. Taylor |
| 2021 | Continuous Doubly Constrained Batch Reinforcement Learning. Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola |
| 2021 | Continuous Latent Process Flows. Ruizhi Deng, Marcus A. Brubaker, Greg Mori, Andreas M. Lehrmann |
| 2021 | Continuous Mean-Covariance Bandits. Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang |
| 2021 | Continuous vs. Discrete Optimization of Deep Neural Networks. Omer Elkabetz, Nadav Cohen |
| 2021 | Continuous-time edge modelling using non-parametric point processes. Xuhui Fan, Bin Li, Feng Zhou, Scott A. Sisson |
| 2021 | Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing. Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das |
| 2021 | Contrastive Active Inference. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt |
| 2021 | Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels. Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong |
| 2021 | Contrastive Laplacian Eigenmaps. Hao Zhu, Ke Sun, Peter Koniusz |
| 2021 | Contrastive Learning for Neural Topic Model. Thong Nguyen, Anh Tuan Luu |
| 2021 | Contrastive Learning of Global and Local Video Representations. Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song |
| 2021 | Contrastive Reinforcement Learning of Symbolic Reasoning Domains. Gabriel Poesia, Wenxin Dong, Noah D. Goodman |
| 2021 | Contrastively Disentangled Sequential Variational Autoencoder. Junwen Bai, Weiran Wang, Carla P. Gomes |
| 2021 | Control Variates for Slate Off-Policy Evaluation. Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus |
| 2021 | Controllable and Compositional Generation with Latent-Space Energy-Based Models. Weili Nie, Arash Vahdat, Anima Anandkumar |
| 2021 | Controlled Text Generation as Continuous Optimization with Multiple Constraints. Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov |
| 2021 | Controlling Neural Networks with Rule Representations. Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister |
| 2021 | Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu |
| 2021 | Convergence and Alignment of Gradient Descent with Random Backpropagation Weights. Ganlin Song, Ruitu Xu, John Lafferty |
| 2021 | Convergence of adaptive algorithms for constrained weakly convex optimization. Ahmet Alacaoglu, Yura Malitsky, Volkan Cevher |
| 2021 | Convex Polytope Trees and its Application to VAE. Mohammadreza Armandpour, Ali Sadeghian, Mingyuan Zhou |
| 2021 | Convex-Concave Min-Max Stackelberg Games. Denizalp Goktas, Amy Greenwald |
| 2021 | Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training. Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu |
| 2021 | Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback. Lin Yang, Yu-Zhen Janice Chen, Stephen Pasteris, Mohammad H. Hajiesmaili, John C. S. Lui, Don Towsley |
| 2021 | Coordinated Proximal Policy Optimization. Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, Haiyin Piao, Hankz Hankui Zhuo |
| 2021 | Coresets for Classification - Simplified and Strengthened. Tung Mai, Cameron Musco, Anup Rao |
| 2021 | Coresets for Clustering with Missing Values. Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu |
| 2021 | Coresets for Decision Trees of Signals. Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, Dan Feldman |
| 2021 | Coresets for Time Series Clustering. Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi |
| 2021 | Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities. Miklós Z. Rácz, Anirudh Sridhar |
| 2021 | Corruption Robust Active Learning. Yifang Chen, Simon S. Du, Kevin Jamieson |
| 2021 | CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction. Léo Lebrat, Rodrigo Santa Cruz, Frédéric de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado |
| 2021 | Cortico-cerebellar networks as decoupling neural interfaces. Joseph Pemberton, Ellen Boven, Richard Apps, Rui Ponte Costa |
| 2021 | Counterbalancing Learning and Strategic Incentives in Allocation Markets. Jamie Kang, Faidra Monachou, Moran Koren, Itai Ashlagi |
| 2021 | Counterexample Guided RL Policy Refinement Using Bayesian Optimization. Briti Gangopadhyay, Pallab Dasgupta |
| 2021 | Counterfactual Explanations Can Be Manipulated. Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh |
| 2021 | Counterfactual Explanations in Sequential Decision Making Under Uncertainty. Stratis Tsirtsis, Abir De, Manuel Gomez Rodriguez |
| 2021 | Counterfactual Invariance to Spurious Correlations in Text Classification. Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein |
| 2021 | Counterfactual Maximum Likelihood Estimation for Training Deep Networks. Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang |
| 2021 | Coupled Gradient Estimators for Discrete Latent Variables. Zhe Dong, Andriy Mnih, George Tucker |
| 2021 | Coupled Segmentation and Edge Learning via Dynamic Graph Propagation. Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas M. Breuel, Anima Anandkumar, Jan Kautz |
| 2021 | Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou |
| 2021 | Credal Self-Supervised Learning. Julian Lienen, Eyke Hüllermeier |
| 2021 | Credit Assignment Through Broadcasting a Global Error Vector. David G. Clark, L. F. Abbott, SueYeon Chung |
| 2021 | Credit Assignment in Neural Networks through Deep Feedback Control. Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe |
| 2021 | Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning. Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, Yang Yu |
| 2021 | Cross-view Geo-localization with Layer-to-Layer Transformer. Hongji Yang, Xiufan Lu, Yingying Zhu |
| 2021 | CrypTen: Secure Multi-Party Computation Meets Machine Learning. Brian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten |
| 2021 | Curriculum Design for Teaching via Demonstrations: Theory and Applications. Gaurav Yengera, Rati Devidze, Parameswaran Kamalaruban, Adish Singla |
| 2021 | Curriculum Disentangled Recommendation with Noisy Multi-feedback. Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu |
| 2021 | Curriculum Learning for Vision-and-Language Navigation. Jiwen Zhang, Zhongyu Wei, Jianqing Fan, Jiajie Peng |
| 2021 | Curriculum Offline Imitating Learning. Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu |
| 2021 | Cycle Self-Training for Domain Adaptation. Hong Liu, Jianmin Wang, Mingsheng Long |
| 2021 | D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation. Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon |
| 2021 | DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar |
| 2021 | DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer. Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang, Sameh Khamis, Or Litany, Sanja Fidler |
| 2021 | DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel. Benjamin Dupuis, Arthur Jacot |
| 2021 | DOBF: A Deobfuscation Pre-Training Objective for Programming Languages. Marie-Anne Lachaux, Baptiste Rozière, Marc Szafraniec, Guillaume Lample |
| 2021 | DOCTOR: A Simple Method for Detecting Misclassification Errors. Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida |
| 2021 | DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples. Yi Xu, Jiandong Ding, Lu Zhang, Shuigeng Zhou |
| 2021 | DRIVE: One-bit Distributed Mean Estimation. Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher |
| 2021 | DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras. Zachary Teed, Jia Deng |
| 2021 | DRONE: Data-aware Low-rank Compression for Large NLP Models. Patrick H. Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2021 | DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi |
| 2021 | Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong |
| 2021 | Dangers of Bayesian Model Averaging under Covariate Shift. Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson |
| 2021 | Data Augmentation Can Improve Robustness. Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy A. Mann |
| 2021 | Data Sharing and Compression for Cooperative Networked Control. Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang |
| 2021 | Data driven semi-supervised learning. Maria-Florina Balcan, Dravyansh Sharma |
| 2021 | Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective. Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang |
| 2021 | Data-Efficient Instance Generation from Instance Discrimination. Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou |
| 2021 | Dataset Distillation with Infinitely Wide Convolutional Networks. Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee |
| 2021 | De-randomizing MCMC dynamics with the diffusion Stein operator. Zheyang Shen, Markus Heinonen, Samuel Kaski |
| 2021 | Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification. Clémence Réda, Andrea Tirinzoni, Rémy Degenne |
| 2021 | Debiased Visual Question Answering from Feature and Sample Perspectives. Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, Qi Wu |
| 2021 | Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data. Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy |
| 2021 | Decentralized Learning in Online Queuing Systems. Flore Sentenac, Etienne Boursier, Vianney Perchet |
| 2021 | Decentralized Q-learning in Zero-sum Markov Games. Muhammed O. Sayin, Kaiqing Zhang, David S. Leslie, Tamer Basar, Asuman E. Ozdaglar |
| 2021 | Decision Transformer: Reinforcement Learning via Sequence Modeling. Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch |
| 2021 | Deconditional Downscaling with Gaussian Processes. Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic |
| 2021 | Deconvolutional Networks on Graph Data. Jia Li, Jiajin Li, Yang Liu, Jianwei Yu, Yueting Li, Hong Cheng |
| 2021 | Decoupling the Depth and Scope of Graph Neural Networks. Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor K. Prasanna, Long Jin, Ren Chen |
| 2021 | Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP. Josh Rozner, Christopher Potts, Kyle Mahowald |
| 2021 | Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks. Rong Zhu, Mattia Rigotti |
| 2021 | Deep Conditional Gaussian Mixture Model for Constrained Clustering. Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt |
| 2021 | Deep Contextual Video Compression. Jiahao Li, Bin Li, Yan Lu |
| 2021 | Deep Explicit Duration Switching Models for Time Series. Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Bernie Wang, Tim Januschowski |
| 2021 | Deep Extended Hazard Models for Survival Analysis. Qixian Zhong, Jonas Mueller, Jane-Ling Wang |
| 2021 | Deep Extrapolation for Attribute-Enhanced Generation. Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik |
| 2021 | Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings. Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu |
| 2021 | Deep Learning Through the Lens of Example Difficulty. Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur |
| 2021 | Deep Learning on a Data Diet: Finding Important Examples Early in Training. Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite |
| 2021 | Deep Learning with Label Differential Privacy. Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang |
| 2021 | Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis. Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, Sanja Fidler |
| 2021 | Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data. Amirreza Farnoosh, Sarah Ostadabbas |
| 2021 | Deep Molecular Representation Learning via Fusing Physical and Chemical Information. Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai |
| 2021 | Deep Networks Provably Classify Data on Curves. Tingran Wang, Sam Buchanan, Dar Gilboa, John Wright |
| 2021 | Deep Neural Networks as Point Estimates for Deep Gaussian Processes. Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande |
| 2021 | Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. Liyuan Xu, Heishiro Kanagawa, Arthur Gretton |
| 2021 | Deep Reinforcement Learning at the Edge of the Statistical Precipice. Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare |
| 2021 | Deep Residual Learning in Spiking Neural Networks. Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian |
| 2021 | Deep Self-Dissimilarities as Powerful Visual Fingerprints. Idan Kligvasser, Tamar Rott Shaham, Yuval Bahat, Tomer Michaeli |
| 2021 | Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess. Gregory Clark |
| 2021 | Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. Feng Zhu, Andrew R. Sedler, Harrison A. Grier, Nauman Ahad, Mark A. Davenport, Matthew T. Kaufman, Andrea Giovannucci, Chethan Pandarinath |
| 2021 | Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. Taiji Suzuki, Atsushi Nitanda |
| 2021 | DeepGEM: Generalized Expectation-Maximization for Blind Inversion. Angela F. Gao, Jorge C. Castellanos, Yisong Yue, Zachary E. Ross, Katherine L. Bouman |
| 2021 | DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning. Hang Xu, Kelly Kostopoulou, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis |
| 2021 | DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales. Brandon G. Jacques, Zoran Tiganj, Marc W. Howard, Per B. Sederberg |
| 2021 | Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks. Woochul Kang, Daeyeon Kim |
| 2021 | Deformable Butterfly: A Highly Structured and Sparse Linear Transform. Rui Lin, Jie Ran, King Hung Chiu, Graziano Chesi, Ngai Wong |
| 2021 | Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han |
| 2021 | Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems. Wenqing Zheng, Qiangqiang Guo, Hao Yang, Peihao Wang, Zhangyang Wang |
| 2021 | Demystifying and Generalizing BinaryConnect. Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia |
| 2021 | Denoising Normalizing Flow. Christian Horvat, Jean-Pascal Pfister |
| 2021 | Dense Keypoints via Multiview Supervision. Zhixuan Yu, Haozheng Yu, Long Sha, Sujoy Ganguly, Hyun Soo Park |
| 2021 | Dense Unsupervised Learning for Video Segmentation. Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth |
| 2021 | Densely connected normalizing flows. Matej Grcic, Ivan Grubisic, Sinisa Segvic |
| 2021 | Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity. Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Basar |
| 2021 | Design of Experiments for Stochastic Contextual Linear Bandits. Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill |
| 2021 | Designing Counterfactual Generators using Deep Model Inversion. Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias |
| 2021 | Detecting Anomalous Event Sequences with Temporal Point Processes. Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann |
| 2021 | Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha |
| 2021 | Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess. Reid McIlroy-Young, Yu Wang, Siddhartha Sen, Jon M. Kleinberg, Ashton Anderson |
| 2021 | Detecting Moments and Highlights in Videos via Natural Language Queries. Jie Lei, Tamara L. Berg, Mohit Bansal |
| 2021 | Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt |
| 2021 | Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD. Rémi Bardenet, Subhroshekhar Ghosh, Meixia Lin |
| 2021 | DiBS: Differentiable Bayesian Structure Learning. Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause |
| 2021 | Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse |
| 2021 | Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games. Shinsaku Sakaue, Kengo Nakamura |
| 2021 | Differentiable Learning Under Triage. Nastaran Okati, Abir De, Manuel Gomez-Rodriguez |
| 2021 | Differentiable Multiple Shooting Layers. Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama |
| 2021 | Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs. Zihang Meng, Lopamudra Mukherjee, Yichao Wu, Vikas Singh, Sathya N. Ravi |
| 2021 | Differentiable Quality Diversity. Matthew C. Fontaine, Stefanos Nikolaidis |
| 2021 | Differentiable Simulation of Soft Multi-body Systems. Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin |
| 2021 | Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks. Yuhang Li, Yufei Guo, Shanghang Zhang, Shikuang Deng, Yongqing Hai, Shi Gu |
| 2021 | Differentiable Spline Approximations. Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde |
| 2021 | Differentiable Synthesis of Program Architectures. Guofeng Cui, He Zhu |
| 2021 | Differentiable Unsupervised Feature Selection based on a Gated Laplacian. Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger |
| 2021 | Differentiable rendering with perturbed optimizers. Quentin Le Lidec, Ivan Laptev, Cordelia Schmid, Justin Carpentier |
| 2021 | Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent. Rishav Chourasia, Jiayuan Ye, Reza Shokri |
| 2021 | Differential Privacy Over Riemannian Manifolds. Matthew Reimherr, Karthik Bharath, Carlos Soto |
| 2021 | Differentially Private Empirical Risk Minimization under the Fairness Lens. Cuong Tran, My H. Dinh, Ferdinando Fioretto |
| 2021 | Differentially Private Federated Bayesian Optimization with Distributed Exploration. Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | Differentially Private Learning with Adaptive Clipping. Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy |
| 2021 | Differentially Private Model Personalization. Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta |
| 2021 | Differentially Private Multi-Armed Bandits in the Shuffle Model. Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2021 | Differentially Private Sampling from Distributions. Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg |
| 2021 | Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings. Raef Bassily, Cristóbal Guzmán, Michael Menart |
| 2021 | Differentially Private n-gram Extraction. Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin |
| 2021 | Diffusion Models Beat GANs on Image Synthesis. Prafulla Dhariwal, Alexander Quinn Nichol |
| 2021 | Diffusion Normalizing Flow. Qinsheng Zhang, Yongxin Chen |
| 2021 | Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet |
| 2021 | Dimension-free empirical entropy estimation. Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer |
| 2021 | Dimensionality Reduction for Wasserstein Barycenter. Zachary Izzo, Sandeep Silwal, Samson Zhou |
| 2021 | Direct Multi-view Multi-person 3D Pose Estimation. Tao Wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng |
| 2021 | Directed Graph Contrastive Learning. Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang |
| 2021 | Directed Probabilistic Watershed. Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht |
| 2021 | Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. Neil Gallagher, Kafui Dzirasa, David E. Carlson |
| 2021 | Directional Message Passing on Molecular Graphs via Synthetic Coordinates. Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann |
| 2021 | Dirichlet Energy Constrained Learning for Deep Graph Neural Networks. Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu |
| 2021 | Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation. Ying Sun, Hengshu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong |
| 2021 | Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks. Iulia Duta, Andrei Liviu Nicolicioiu, Marius Leordeanu |
| 2021 | Discovering and Achieving Goals via World Models. Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak |
| 2021 | Discovery of Options via Meta-Learned Subgoals. Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh |
| 2021 | Discrete-Valued Neural Communication. Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio |
| 2021 | Disentangled Contrastive Learning on Graphs. Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu |
| 2021 | Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen |
| 2021 | Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect. Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann |
| 2021 | Disrupting Deep Uncertainty Estimation Without Harming Accuracy. Ido Galil, Ran El-Yaniv |
| 2021 | Dissecting the Diffusion Process in Linear Graph Convolutional Networks. Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2021 | Distilling Image Classifiers in Object Detectors. Shuxuan Guo, José M. Álvarez, Mathieu Salzmann |
| 2021 | Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media. Yiyue Qian, Yiming Zhang, Yanfang Ye, Chuxu Zhang |
| 2021 | Distilling Object Detectors with Feature Richness. Zhixing Du, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen |
| 2021 | Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck. Junho Kim, Byung-Kwan Lee, Yong Man Ro |
| 2021 | Distributed Deep Learning In Open Collaborations. 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 |
| 2021 | Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition. Jayadev Acharya, Clément L. Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi |
| 2021 | Distributed Machine Learning with Sparse Heterogeneous Data. Dominic Richards, Sahand Negahban, Patrick Rebeschini |
| 2021 | Distributed Principal Component Analysis with Limited Communication. Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh |
| 2021 | Distributed Saddle-Point Problems Under Data Similarity. Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov |
| 2021 | Distributed Zero-Order Optimization under Adversarial Noise. Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov |
| 2021 | Distribution-free inference for regression: discrete, continuous, and in between. Yonghoon Lee, Rina Barber |
| 2021 | Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models. Lenart Treven, Philippe Wenk, Florian Dörfler, Andreas Krause |
| 2021 | Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu |
| 2021 | Distributionally Robust Imitation Learning. Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang |
| 2021 | Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals. Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui |
| 2021 | Diverse Message Passing for Attribute with Heterophily. Liang Yang, Mengzhe Li, Liyang Liu, Bingxin Niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo |
| 2021 | Diversity Enhanced Active Learning with Strictly Proper Scoring Rules. Wei Tan, Lan Du, Wray L. Buntine |
| 2021 | Diversity Matters When Learning From Ensembles. Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee |
| 2021 | Do Different Tracking Tasks Require Different Appearance Models? Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto |
| 2021 | Do Input Gradients Highlight Discriminative Features? Harshay Shah, Prateek Jain, Praneeth Netrapalli |
| 2021 | Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark. Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M. Solomon, Alexander Filippov, Evgeny Burnaev |
| 2021 | Do Transformers Really Perform Badly for Graph Representation? Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu |
| 2021 | Do Vision Transformers See Like Convolutional Neural Networks? Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy |
| 2021 | Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu |
| 2021 | Does Knowledge Distillation Really Work? Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson |
| 2021 | Does Preprocessing Help Training Over-parameterized Neural Networks? Zhao Song, Shuo Yang, Ruizhe Zhang |
| 2021 | Does enforcing fairness mitigate biases caused by subpopulation shift? Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun |
| 2021 | Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang |
| 2021 | Domain Invariant Representation Learning with Domain Density Transformations. A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin |
| 2021 | DominoSearch: Find layer-wise fine-grained N: M sparse schemes from dense neural networks. Wei Sun, Aojun Zhou, Sander Stuijk, Rob G. J. Wijnhoven, Andrew Nelson, Hongsheng Li, Henk Corporaal |
| 2021 | Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence. Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis |
| 2021 | Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. Yonghan Jung, Jin Tian, Elias Bareinboim |
| 2021 | Double/Debiased Machine Learning for Dynamic Treatment Effects. Greg Lewis, Vasilis Syrgkanis |
| 2021 | Doubly Robust Thompson Sampling with Linear Payoffs. Wonyoung Kim, Gi-Soo Kim, Myunghee Cho Paik |
| 2021 | Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates. Romain Laroche, Remi Tachet des Combes |
| 2021 | Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks. Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David D. Cox, Yingyan Lin |
| 2021 | Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer |
| 2021 | Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers. Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson |
| 2021 | DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks. Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer |
| 2021 | Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions. Lijun Zhang, Guanghui Wang, Wei-Wei Tu, Wei Jiang, Zhi-Hua Zhou |
| 2021 | Dual Parameterization of Sparse Variational Gaussian Processes. Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin |
| 2021 | Dual Progressive Prototype Network for Generalized Zero-Shot Learning. Chaoqun Wang, Shaobo Min, Xuejin Chen, Xiaoyan Sun, Houqiang Li |
| 2021 | Dual-stream Network for Visual Recognition. Mingyuan Mao, Peng Gao, Renrui Zhang, Honghui Zheng, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han |
| 2021 | DualNet: Continual Learning, Fast and Slow. Quang Pham, Chenghao Liu, Steven C. H. Hoi |
| 2021 | Dueling Bandits with Adversarial Sleeping. Aadirupa Saha, Pierre Gaillard |
| 2021 | Dueling Bandits with Team Comparisons. Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour |
| 2021 | Duplex Sequence-to-Sequence Learning for Reversible Machine Translation. Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, Lei Li |
| 2021 | Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking. Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush, Somya Jain, Ledell Wu, Robin Jia, Christopher Potts, Adina Williams, Douwe Kiela |
| 2021 | Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit. Shoutik Mukherjee, Behtash Babadi |
| 2021 | Dynamic Bottleneck for Robust Self-Supervised Exploration. Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang |
| 2021 | Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model. Yuan Chen, Wenbo Fei, Qinxia Wang, Donglin Zeng, Yuanjia Wang |
| 2021 | Dynamic Causal Bayesian Optimization. Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas |
| 2021 | Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data. Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Richard J. Radke |
| 2021 | Dynamic Grained Encoder for Vision Transformers. Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng |
| 2021 | Dynamic Inference with Neural Interpreters. Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf |
| 2021 | Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation. Bowen Zhang, Yifan Liu, Zhi Tian, Chunhua Shen |
| 2021 | Dynamic Normalization and Relay for Video Action Recognition. Dongqi Cai, Anbang Yao, Yurong Chen |
| 2021 | Dynamic Resolution Network. Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang |
| 2021 | Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares. Hiroaki Yamada, Makoto Yamada |
| 2021 | Dynamic Trace Estimation. Prathamesh Dharangutte, Christopher Musco |
| 2021 | Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language. Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, Chuang Gan |
| 2021 | Dynamic influence maximization. Binghui Peng |
| 2021 | Dynamic population-based meta-learning for multi-agent communication with natural language. Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou |
| 2021 | DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification. Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh |
| 2021 | Dynamical Wasserstein Barycenters for Time-series Modeling. Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller |
| 2021 | Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. Courtney Paquette, Elliot Paquette |
| 2021 | Dynamics-regulated kinematic policy for egocentric pose estimation. Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Kris Kitani |
| 2021 | E(n) Equivariant Normalizing Flows. Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling |
| 2021 | EDGE: Explaining Deep Reinforcement Learning Policies. Wenbo Guo, Xian Wu, Usmann Khan, Xinyu Xing |
| 2021 | EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin |
| 2021 | EIGNN: Efficient Infinite-Depth Graph Neural Networks. Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao |
| 2021 | ELLA: Exploration through Learned Language Abstraction. Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh |
| 2021 | Early Convolutions Help Transformers See Better. Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, Ross B. Girshick |
| 2021 | Early-stopped neural networks are consistent. Ziwei Ji, Justin D. Li, Matus Telgarsky |
| 2021 | Edge Representation Learning with Hypergraphs. Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, Sung Ju Hwang |
| 2021 | EditGAN: High-Precision Semantic Image Editing. Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler |
| 2021 | Editing a classifier by rewriting its prediction rules. Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry |
| 2021 | Effective Meta-Regularization by Kernelized Proximal Regularization. Weisen Jiang, James T. Kwok, Yu Zhang |
| 2021 | Efficient Active Learning for Gaussian Process Classification by Error Reduction. Guang Zhao, Edward R. Dougherty, Byung-Jun Yoon, Francis J. Alexander, Xiaoning Qian |
| 2021 | Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan |
| 2021 | Efficient Bayesian network structure learning via local Markov boundary search. Ming Gao, Bryon Aragam |
| 2021 | Efficient Combination of Rematerialization and Offloading for Training DNNs. Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova |
| 2021 | Efficient Equivariant Network. Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin |
| 2021 | Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. Dylan J. Foster, Akshay Krishnamurthy |
| 2021 | Efficient Generalization with Distributionally Robust Learning. Soumyadip Ghosh, Mark S. Squillante, Ebisa D. Wollega |
| 2021 | Efficient Learning of Discrete-Continuous Computation Graphs. David Friede, Mathias Niepert |
| 2021 | Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems. Feihu Huang, Xidong Wu, Heng Huang |
| 2021 | Efficient Neural Network Training via Forward and Backward Propagation Sparsification. Xiao Zhou, Weizhong Zhang, Zonghao Chen, Shizhe Diao, Tong Zhang |
| 2021 | Efficient Online Estimation of Causal Effects by Deciding What to Observe. Shantanu Gupta, Zachary C. Lipton, David Childers |
| 2021 | Efficient Statistical Assessment of Neural Network Corruption Robustness. Karim Tit, Teddy Furon, Mathias Rousset |
| 2021 | Efficient Training of Retrieval Models using Negative Cache. Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar |
| 2021 | Efficient Training of Visual Transformers with Small Datasets. Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai |
| 2021 | Efficient Truncated Linear Regression with Unknown Noise Variance. Constantinos Daskalakis, Patroklos Stefanou, Rui Yao, Emmanouil Zampetakis |
| 2021 | Efficient and Accurate Gradients for Neural SDEs. Patrick Kidger, James Foster, Xuechen Li, Terry J. Lyons |
| 2021 | Efficient and Local Parallel Random Walks. Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos |
| 2021 | Efficient constrained sampling via the mirror-Langevin algorithm. Kwangjun Ahn, Sinho Chewi |
| 2021 | Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging. Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe |
| 2021 | Efficient methods for Gaussian Markov random fields under sparse linear constraints. David Bolin, Jonas Wallin |
| 2021 | Efficiently Identifying Task Groupings for Multi-Task Learning. Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn |
| 2021 | Efficiently Learning One Hidden Layer ReLU Networks From Queries. Sitan Chen, Adam R. Klivans, Raghu Meka |
| 2021 | Embedding Principle of Loss Landscape of Deep Neural Networks. Yaoyu Zhang, Zhongwang Zhang, Tao Luo, Zhi-Qin John Xu |
| 2021 | Emergent Communication of Generalizations. Jesse Mu, Noah D. Goodman |
| 2021 | Emergent Communication under Varying Sizes and Connectivities. Jooyeon Kim, Alice Oh |
| 2021 | Emergent Discrete Communication in Semantic Spaces. Mycal Tucker, Huao Li, Siddharth Agrawal, Dana Hughes, Katia P. Sycara, Michael Lewis, Julie A. Shah |
| 2021 | Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath |
| 2021 | Encoding Robustness to Image Style via Adversarial Feature Perturbations. Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein |
| 2021 | Encoding Spatial Distribution of Convolutional Features for Texture Representation. Yong Xu, Feng Li, Zhile Chen, Jinxiu Liang, Yuhui Quan |
| 2021 | End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering. Devendra Singh Sachan, Siva Reddy, William L. Hamilton, Chris Dyer, Dani Yogatama |
| 2021 | End-to-End Weak Supervision. Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski |
| 2021 | End-to-end Multi-modal Video Temporal Grounding. Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang |
| 2021 | End-to-end reconstruction meets data-driven regularization for inverse problems. Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb |
| 2021 | Ensembling Graph Predictions for AMR Parsing. Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo |
| 2021 | Entropic Desired Dynamics for Intrinsic Control. Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih |
| 2021 | Entropy-based adaptive Hamiltonian Monte Carlo. Marcel Hirt, Michalis K. Titsias, Petros Dellaportas |
| 2021 | Environment Generation for Zero-Shot Compositional Reinforcement Learning. Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust |
| 2021 | Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration. Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang |
| 2021 | Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium. Gabriele Farina, Tuomas Sandholm |
| 2021 | Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines. Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane |
| 2021 | Equivariant Manifold Flows. Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa |
| 2021 | Error Compensated Distributed SGD Can Be Accelerated. Xun Qian, Peter Richtárik, Tong Zhang |
| 2021 | ErrorCompensatedX: error compensation for variance reduced algorithms. Hanlin Tang, Yao Li, Ji Liu, Ming Yan |
| 2021 | Escape saddle points by a simple gradient-descent based algorithm. Chenyi Zhang, Tongyang Li |
| 2021 | Escaping Saddle Points with Compressed SGD. Dmitrii Avdiukhin, Grigory Yaroslavtsev |
| 2021 | Estimating High Order Gradients of the Data Distribution by Denoising. Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon |
| 2021 | Estimating Multi-cause Treatment Effects via Single-cause Perturbation. Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar |
| 2021 | Estimating the Long-Term Effects of Novel Treatments. Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis |
| 2021 | Estimating the Unique Information of Continuous Variables. Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman |
| 2021 | Evaluating Efficient Performance Estimators of Neural Architectures. Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, Yu Wang |
| 2021 | Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora |
| 2021 | Evaluating State-of-the-Art Classification Models Against Bayes Optimality. Ryan Theisen, Huan Wang, Lav R. Varshney, Caiming Xiong, Richard Socher |
| 2021 | Evaluating model performance under worst-case subpopulations. Mike Li, Hongseok Namkoong, Shangzhou Xia |
| 2021 | Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi. Ho Chit Siu, Jaime Daniel Peña, Edenna Chen, Yutai Zhou, Victor J. Lopez, Kyle Palko, Kimberlee C. Chang, Ross E. Allen |
| 2021 | Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation. Kenneth Borup, Lars Nørvang Andersen |
| 2021 | Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models. Phil Chen, Masha Itkina, Ransalu Senanayake, Mykel J. Kochenderfer |
| 2021 | EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization. Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales |
| 2021 | Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots. Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik |
| 2021 | Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms. Jeremy Seeman, Matthew Reimherr, Aleksandra B. Slavkovic |
| 2021 | Exact marginal prior distributions of finite Bayesian neural networks. Jacob A. Zavatone-Veth, Cengiz Pehlevan |
| 2021 | Excess Capacity and Backdoor Poisoning. Naren Manoj, Avrim Blum |
| 2021 | Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning. Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu |
| 2021 | Explaining Hyperparameter Optimization via Partial Dependence Plots. Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl |
| 2021 | Explaining Latent Representations with a Corpus of Examples. Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar |
| 2021 | Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. Aran Nayebi, Alexander Attinger, Malcolm Campbell, Kiah Hardcastle, Isabel Low, Caitlin S. Mallory, Gabriel Mel, Ben Sorscher, Alex H. Williams, Surya Ganguli, Lisa M. Giocomo, Daniel L. K. Yamins |
| 2021 | Explanation-based Data Augmentation for Image Classification. Sandareka Wickramanayake, Wynne Hsu, Mong-Li Lee |
| 2021 | Explicable Reward Design for Reinforcement Learning Agents. Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla |
| 2021 | Explicit loss asymptotics in the gradient descent training of neural networks. Maksim Velikanov, Dmitry Yarotsky |
| 2021 | Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions. Huangjie Zheng, Mingyuan Zhou |
| 2021 | Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation. Jinming Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Li Wang |
| 2021 | Exploiting Domain-Specific Features to Enhance Domain Generalization. Manh-Ha Bui, Toan Tran, Anh Tran, Dinh Q. Phung |
| 2021 | Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. Qiujiang Jin, Aryan Mokhtari |
| 2021 | Exploiting Opponents Under Utility Constraints in Sequential Games. Martino Bernasconi de Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi, Francesco Trovò |
| 2021 | Exploiting a Zoo of Checkpoints for Unseen Tasks. Jiaji Huang, Qiang Qiu, Kenneth Church |
| 2021 | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation. Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, Shangling Jui |
| 2021 | Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality. Stefanos Leonardos, Georgios Piliouras, Kelly Spendlove |
| 2021 | Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma |
| 2021 | Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing. Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang |
| 2021 | Exploring Forensic Dental Identification with Deep Learning. Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting Shao, Kun Wang, Lei He |
| 2021 | Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling. Chen Tang, Wei Zhan, Masayoshi Tomizuka |
| 2021 | Exploring the Limits of Out-of-Distribution Detection. Stanislav Fort, Jie Ren, Balaji Lakshminarayanan |
| 2021 | Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning. Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang |
| 2021 | Exponential Graph is Provably Efficient for Decentralized Deep Training. Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin |
| 2021 | Exponential Separation between Two Learning Models and Adversarial Robustness. Grzegorz Gluch, Rüdiger L. Urbanke |
| 2021 | Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models. Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty |
| 2021 | Extracting Deformation-Aware Local Features by Learning to Deform. Guilherme A. Potje, Renato Martins, Felipe C. Chamone, Erickson R. Nascimento |
| 2021 | FACMAC: Factored Multi-Agent Centralised Policy Gradients. Bei Peng, Tabish Rashid, Christian Schröder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson |
| 2021 | FINE Samples for Learning with Noisy Labels. Taehyeon Kim, Jongwoo Ko, Sangwook Cho, Jinhwan Choi, Se-Young Yun |
| 2021 | FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, Hai Li |
| 2021 | FLEX: Unifying Evaluation for Few-Shot NLP. Jonathan Bragg, Arman Cohan, Kyle Lo, Iz Beltagy |
| 2021 | FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang |
| 2021 | Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs. Thomas Spooner, Nelson Vadori, Sumitra Ganesh |
| 2021 | Fair Algorithms for Multi-Agent Multi-Armed Bandits. Safwan Hossain, Evi Micha, Nisarg Shah |
| 2021 | Fair Classification with Adversarial Perturbations. L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi |
| 2021 | Fair Clustering Under a Bounded Cost. Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson |
| 2021 | Fair Exploration via Axiomatic Bargaining. Jackie Baek, Vivek F. Farias |
| 2021 | Fair Scheduling for Time-dependent Resources. Bo Li, Minming Li, Ruilong Zhang |
| 2021 | Fair Sequential Selection Using Supervised Learning Models. Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan |
| 2021 | Fair Sortition Made Transparent. Bailey Flanigan, Gregory Kehne, Ariel D. Procaccia |
| 2021 | Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. Adarsh Barik, Jean Honorio |
| 2021 | Fairness in Ranking under Uncertainty. Ashudeep Singh, David Kempe, Thorsten Joachims |
| 2021 | Fairness via Representation Neutralization. Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, Xia Ben Hu |
| 2021 | Fast Abductive Learning by Similarity-based Consistency Optimization. Yu-Xuan Huang, Wang-Zhou Dai, Le-Wen Cai, Stephen H. Muggleton, Yuan Jiang |
| 2021 | Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs. Bahram Behzadian, Marek Petrik, Chin Pang Ho |
| 2021 | Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes. Mohamad Amin Sharifi Kolarijani, G. F. Max, Peyman Mohajerin Esfahani |
| 2021 | Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli |
| 2021 | Fast Axiomatic Attribution for Neural Networks. Robin Hesse, Simone Schaub-Meyer, Stefan Roth |
| 2021 | Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation. Hideaki Kim |
| 2021 | Fast Certified Robust Training with Short Warmup. Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh |
| 2021 | Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds. Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal |
| 2021 | Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems. Sucheol Lee, Donghwan Kim |
| 2021 | Fast Federated Learning in the Presence of Arbitrary Device Unavailability. Xinran Gu, Kaixuan Huang, Jingzhao Zhang, Longbo Huang |
| 2021 | Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints. Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio |
| 2021 | Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification. Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon |
| 2021 | Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization. Shicong Cen, Yuting Wei, Yuejie Chi |
| 2021 | Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics. Andersen Man Shun Ang, Jianzhu Ma, Nianjun Liu, Kun Huang, Yijie Wang |
| 2021 | Fast Pure Exploration via Frank-Wolfe. Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutière |
| 2021 | Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights. Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos |
| 2021 | Fast Training Method for Stochastic Compositional Optimization Problems. Hongchang Gao, Heng Huang |
| 2021 | Fast Training of Neural Lumigraph Representations using Meta Learning. Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzstein |
| 2021 | Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation. Kazu Ghalamkari, Mahito Sugiyama |
| 2021 | Fast and Memory Efficient Differentially Private-SGD via JL Projections. Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat |
| 2021 | Fast and accurate randomized algorithms for low-rank tensor decompositions. Linjian Ma, Edgar Solomonik |
| 2021 | Fast rates for prediction with limited expert advice. El Mehdi Saad, Gilles Blanchard |
| 2021 | FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition. Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu |
| 2021 | Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error. Jonah Brown-Cohen |
| 2021 | Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. Dachao Lin, Ruoyu Sun, Zhihua Zhang |
| 2021 | Faster Matchings via Learned Duals. Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii |
| 2021 | Faster Neural Network Training with Approximate Tensor Operations. Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein |
| 2021 | Faster Non-asymptotic Convergence for Double Q-learning. Lin Zhao, Huaqing Xiong, Yingbin Liang |
| 2021 | Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods. Hamza Fawzi, Harry Goulbourne |
| 2021 | Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low |
| 2021 | FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen |
| 2021 | Federated Graph Classification over Non-IID Graphs. Han Xie, Jing Ma, Li Xiong, Carl Yang |
| 2021 | Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar |
| 2021 | Federated Linear Contextual Bandits. Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen |
| 2021 | Federated Multi-Task Learning under a Mixture of Distributions. Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal |
| 2021 | Federated Reconstruction: Partially Local Federated Learning. Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, Sushant Prakash |
| 2021 | Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye |
| 2021 | Federated-EM with heterogeneity mitigation and variance reduction. Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin |
| 2021 | Few-Round Learning for Federated Learning. Younghyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon |
| 2021 | Few-Shot Data-Driven Algorithms for Low Rank Approximation. Piotr Indyk, Tal Wagner, David P. Woodruff |
| 2021 | Few-Shot Object Detection via Association and DIscrimination. Yuhang Cao, Jiaqi Wang, Ying Jin, Tong Wu, Kai Chen, Ziwei Liu, Dahua Lin |
| 2021 | Few-Shot Segmentation via Cycle-Consistent Transformer. Gengwei Zhang, Guoliang Kang, Yi Yang, Yunchao Wei |
| 2021 | Finding Bipartite Components in Hypergraphs. Peter Macgregor, He Sun |
| 2021 | Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution. Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan |
| 2021 | Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks. Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang |
| 2021 | Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag |
| 2021 | Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information. Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab |
| 2021 | Fine-Grained Zero-Shot Learning with DNA as Side Information. Sarkhan Badirli, Zeynep Akata, George O. Mohler, Christine Picard, Murat Dundar |
| 2021 | Fine-grained Generalization Analysis of Inductive Matrix Completion. Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft |
| 2021 | Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning. Sheng Zhang, Zhe Zhang, Siva Theja Maguluri |
| 2021 | Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam |
| 2021 | Fitting summary statistics of neural data with a differentiable spiking network simulator. Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner |
| 2021 | Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. Ruihan Wu, Chuan Guo, Awni Y. Hannun, Laurens van der Maaten |
| 2021 | FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. Samuel Horváth, Stefanos Laskaridis, Mário Almeida, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane |
| 2021 | Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning. Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, Pheng-Ann Heng |
| 2021 | FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki |
| 2021 | Flexible Option Learning. Martin Klissarov, Doina Precup |
| 2021 | Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio |
| 2021 | Focal Attention for Long-Range Interactions in Vision Transformers. Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao |
| 2021 | For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. Brian L. Trippe, Hilary K. Finucane, Tamara Broderick |
| 2021 | Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations. Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen |
| 2021 | Formalizing the Generalization-Forgetting Trade-off in Continual Learning. Krishnan Raghavan, Prasanna Balaprakash |
| 2021 | Forster Decomposition and Learning Halfspaces with Noise. Ilias Diakonikolas, Daniel Kane, Christos Tzamos |
| 2021 | Foundations of Symbolic Languages for Model Interpretability. Marcelo Arenas, Daniel Báez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux |
| 2021 | Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu |
| 2021 | Framing RNN as a kernel method: A neural ODE approach. Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau |
| 2021 | From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu |
| 2021 | From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits. Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard |
| 2021 | From global to local MDI variable importances for random forests and when they are Shapley values. Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts |
| 2021 | Functional Neural Networks for Parametric Image Restoration Problems. Fangzhou Luo, Xiaolin Wu, Yanhui Guo |
| 2021 | Functional Regularization for Reinforcement Learning via Learned Fourier Features. Alexander C. Li, Deepak Pathak |
| 2021 | Functional Variational Inference based on Stochastic Process Generators. Chao Ma, José Miguel Hernández-Lobato |
| 2021 | Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery. Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui Li |
| 2021 | Fuzzy Clustering with Similarity Queries. Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal |
| 2021 | G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators. Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li |
| 2021 | GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement. Martin Engelcke, Oiwi Parker Jones, Ingmar Posner |
| 2021 | GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction. Longyuan Li, Jian Yao, Li K. Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David Wipf, Zheng Zhang |
| 2021 | Garment4D: Garment Reconstruction from Point Cloud Sequences. Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu |
| 2021 | Gauge Equivariant Transformer. Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin |
| 2021 | Gaussian Kernel Mixture Network for Single Image Defocus Deblurring. Yuhui Quan, Zicong Wu, Hui Ji |
| 2021 | GemNet: Universal Directional Graph Neural Networks for Molecules. Johannes Gasteiger, Florian Becker, Stephan Günnemann |
| 2021 | General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds. Haixiang Zhang, Yingjie Bi, Javad Lavaei |
| 2021 | General Nonlinearities in SO(2)-Equivariant CNNs. Daniel Franzen, Michael Wand |
| 2021 | Generalizable Imitation Learning from Observation via Inferring Goal Proximity. Youngwoon Lee, Andrew Szot, Shao-Hua Sun, Joseph J. Lim |
| 2021 | Generalizable Multi-linear Attention Network. Tao Jin, Zhou Zhao |
| 2021 | Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. Qi Chen, Changjian Shui, Mario Marchand |
| 2021 | Generalization Bounds for (Wasserstein) Robust Optimization. Yang An, Rui Gao |
| 2021 | Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza |
| 2021 | Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability. Alec Farid, Anirudha Majumdar |
| 2021 | Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová |
| 2021 | Generalization Guarantee of SGD for Pairwise Learning. Yunwen Lei, Mingrui Liu, Yiming Ying |
| 2021 | Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar |
| 2021 | Generalized DataWeighting via Class-Level Gradient Manipulation. Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue (Steve) Liu, Hao Liu, Dejing Dou |
| 2021 | Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks. Hassan Dbouk, Naresh R. Shanbhag |
| 2021 | Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels. Erik Englesson, Hossein Azizpour |
| 2021 | Generalized Linear Bandits with Local Differential Privacy. Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang |
| 2021 | Generalized Proximal Policy Optimization with Sample Reuse. James Queeney, Yannis Paschalidis, Christos G. Cassandras |
| 2021 | Generalized Shape Metrics on Neural Representations. Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman |
| 2021 | Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement. Aming Wu, Suqi Zhao, Cheng Deng, Wei Liu |
| 2021 | Generating High-Quality Explanations for Navigation in Partially-Revealed Environments. Gregory J. Stein |
| 2021 | Generative Occupancy Fields for 3D Surface-Aware Image Synthesis. Xudong Xu, Xingang Pan, Dahua Lin, Bo Dai |
| 2021 | Generative vs. Discriminative: Rethinking The Meta-Continual Learning. Mohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, Mahdieh Soleymani |
| 2021 | Generic Neural Architecture Search via Regression. Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen |
| 2021 | GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. Octavian Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola |
| 2021 | Geometry Processing with Neural Fields. Guandao Yang, Serge J. Belongie, Bharath Hariharan, Vladlen Koltun |
| 2021 | Glance-and-Gaze Vision Transformer. Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan L. Yuille, Wei Shen |
| 2021 | Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. Tian Ye, Simon S. Du |
| 2021 | Global Convergence of Online Optimization for Nonlinear Model Predictive Control. Sen Na |
| 2021 | Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang |
| 2021 | Global Filter Networks for Image Classification. Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou |
| 2021 | Global-aware Beam Search for Neural Abstractive Summarization. Ye Ma, Zixun Lan, Lu Zong, Kaizhu Huang |
| 2021 | Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning. Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-Hwan Ryu, Minsu Lee, Byoung-Tak Zhang |
| 2021 | Going Beyond Linear RL: Sample Efficient Neural Function Approximation. Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang |
| 2021 | Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber |
| 2021 | Gone Fishing: Neural Active Learning with Fisher Embeddings. Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade |
| 2021 | Good Classification Measures and How to Find Them. Martijn Gösgens, Anton Zhiyanov, Aleksey Tikhonov, Liudmila Prokhorenkova |
| 2021 | Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation. Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno |
| 2021 | GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein |
| 2021 | Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora |
| 2021 | Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low |
| 2021 | Gradient Inversion with Generative Image Prior. Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok |
| 2021 | Gradient Starvation: A Learning Proclivity in Neural Networks. Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie |
| 2021 | Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin |
| 2021 | Gradient-based Editing of Memory Examples for Online Task-free Continual Learning. Xisen Jin, Arka Sadhu, Junyi Du, Xiang Ren |
| 2021 | Gradient-based Hyperparameter Optimization Over Long Horizons. Paul Micaelli, Amos J. Storkey |
| 2021 | Gradual Domain Adaptation without Indexed Intermediate Domains. Hong-You Chen, Wei-Lun Chao |
| 2021 | Grammar-Based Grounded Lexicon Learning. Jiayuan Mao, Freda Shi, Jiajun Wu, Roger Levy, Josh Tenenbaum |
| 2021 | Graph Adversarial Self-Supervised Learning. Longqi Yang, Liangliang Zhang, Wenjing Yang |
| 2021 | Graph Differentiable Architecture Search with Structure Learning. Yijian Qin, Xin Wang, Zeyang Zhang, Wenwu Zhu |
| 2021 | Graph Neural Networks with Adaptive Residual. Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang |
| 2021 | Graph Neural Networks with Local Graph Parameters. Pablo Barceló, Floris Geerts, Juan L. Reutter, Maksimilian Ryschkov |
| 2021 | Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann |
| 2021 | GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph. Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie |
| 2021 | Graphical Models in Heavy-Tailed Markets. José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar |
| 2021 | Greedy Approximation Algorithms for Active Sequential Hypothesis Testing. Kyra Gan, Su Jia, Andrew A. Li |
| 2021 | Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. Dachao Lin, Haishan Ye, Zhihua Zhang |
| 2021 | Grounding Representation Similarity Through Statistical Testing. Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt |
| 2021 | Grounding Spatio-Temporal Language with Transformers. Tristan Karch, Laetitia Teodorescu, Katja Hofmann, Clément Moulin-Frier, Pierre-Yves Oudeyer |
| 2021 | Grounding inductive biases in natural images: invariance stems from variations in data. Diane Bouchacourt, Mark Ibrahim, Ari S. Morcos |
| 2021 | Group Equivariant Subsampling. Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh |
| 2021 | H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion. Hongyi Xu, Thiemo Alldieck, Cristian Sminchisescu |
| 2021 | HNPE: Leveraging Global Parameters for Neural Posterior Estimation. Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort |
| 2021 | HRFormer: High-Resolution Vision Transformer for Dense Predict. Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, Jingdong Wang |
| 2021 | HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning. Shiming Chen, Guo-Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao |
| 2021 | Habitat 2.0: Training Home Assistants to Rearrange their Habitat. 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 |
| 2021 | Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling. Greg Ver Steeg, Aram Galstyan |
| 2021 | Handling Long-tailed Feature Distribution in AdderNets. Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu |
| 2021 | Hard-Attention for Scalable Image Classification. Athanasios Papadopoulos, Pawel Korus, Nasir D. Memon |
| 2021 | Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning. Hayeon Lee, Sewoong Lee, Song Chong, Sung Ju Hwang |
| 2021 | Hash Layers For Large Sparse Models. Stephen Roller, Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston |
| 2021 | Heavy Ball Momentum for Conditional Gradient. Bingcong Li, Alireza Sadeghi, Georgios B. Giannakis |
| 2021 | Heavy Ball Neural Ordinary Differential Equations. Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang |
| 2021 | Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli |
| 2021 | Hessian Eigenspectra of More Realistic Nonlinear Models. Zhenyu Liao, Michael W. Mahoney |
| 2021 | Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang |
| 2021 | Heuristic-Guided Reinforcement Learning. Ching-An Cheng, Andrey Kolobov, Adith Swaminathan |
| 2021 | Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs. Bogdan-Adrian Manghiuc, He Sun |
| 2021 | Hierarchical Reinforcement Learning with Timed Subgoals. Nico Gürtler, Dieter Büchler, Georg Martius |
| 2021 | Hierarchical Skills for Efficient Exploration. Jonas Gehring, Gabriel Synnaeve, Andreas Krause, Nicolas Usunier |
| 2021 | High Probability Complexity Bounds for Line Search Based on Stochastic Oracles. Billy Jin, Katya Scheinberg, Miaolan Xie |
| 2021 | High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails. Ashok Cutkosky, Harsh Mehta |
| 2021 | Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes. Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Horvath, Theodoros Damoulas, Terry J. Lyons |
| 2021 | Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL. Charles Packer, Pieter Abbeel, Joseph E. Gonzalez |
| 2021 | History Aware Multimodal Transformer for Vision-and-Language Navigation. Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, Ivan Laptev |
| 2021 | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation. Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang |
| 2021 | How Data Augmentation affects Optimization for Linear Regression. Boris Hanin, Yi Sun |
| 2021 | How Does it Sound? Kun Su, Xiulong Liu, Eli Shlizerman |
| 2021 | How Fine-Tuning Allows for Effective Meta-Learning. Kurtland Chua, Qi Lei, Jason D. Lee |
| 2021 | How Modular should Neural Module Networks Be for Systematic Generalization? Vanessa D'Amario, Tomotake Sasaki, Xavier Boix |
| 2021 | How Powerful are Performance Predictors in Neural Architecture Search? Colin White, Arber Zela, Robin Ru, Yang Liu, Frank Hutter |
| 2021 | How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? Xinshuai Dong, Anh Tuan Luu, Min Lin, Shuicheng Yan, Hanwang Zhang |
| 2021 | How Tight Can PAC-Bayes be in the Small Data Regime? Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner |
| 2021 | How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel |
| 2021 | How can classical multidimensional scaling go wrong? Rishi Sonthalia, Greg Van Buskirk, Benjamin Raichel, Anna C. Gilbert |
| 2021 | How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, Jimmy Ba |
| 2021 | How to transfer algorithmic reasoning knowledge to learn new algorithms? Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang |
| 2021 | Human-Adversarial Visual Question Answering. Sasha Sheng, Amanpreet Singh, Vedanuj Goswami, Jose Alberto Lopez Magana, Tristan Thrush, Wojciech Galuba, Devi Parikh, Douwe Kiela |
| 2021 | Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits. Shinji Ito |
| 2021 | HyperSPNs: Compact and Expressive Probabilistic Circuits. Andy Shih, Dorsa Sadigh, Stefano Ermon |
| 2021 | Hyperbolic Busemann Learning with Ideal Prototypes. Mina Ghadimi Atigh, Martin Keller-Ressel, Pascal Mettes |
| 2021 | Hyperbolic Procrustes Analysis Using Riemannian Geometry. Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon |
| 2021 | Hypergraph Propagation and Community Selection for Objects Retrieval. Guoyuan An, Yuchi Huo, Sung Eui Yoon |
| 2021 | Hyperparameter Optimization Is Deceiving Us, and How to Stop It. A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa |
| 2021 | Hyperparameter Tuning is All You Need for LISTA. Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin |
| 2021 | IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers. Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogério Feris, Aude Oliva |
| 2021 | INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding. Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, Egor V. Kostylev |
| 2021 | IQ-Learn: Inverse soft-Q Learning for Imitation. Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon |
| 2021 | IRM - when it works and when it doesn't: A test case of natural language inference. Yana Dranker, He He, Yonatan Belinkov |
| 2021 | Identifiability in inverse reinforcement learning. Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch |
| 2021 | Identifiable Generative models for Missing Not at Random Data Imputation. Chao Ma, Cheng Zhang |
| 2021 | Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information. Ryusei Shingaki, Manabu Kuroki |
| 2021 | Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. Jeffrey Adams, Niels Richard Hansen, Kun Zhang |
| 2021 | Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier |
| 2021 | Identifying and Benchmarking Natural Out-of-Context Prediction Problems. David Madras, Richard S. Zemel |
| 2021 | Identity testing for Mallows model. Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Emmanouil Zampetakis |
| 2021 | Image Generation using Continuous Filter Atoms. Ze Wang, Seunghyun Hwang, Zichen Miao, Qiang Qiu |
| 2021 | ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. Patrick Esser, Robin Rombach, Andreas Blattmann, Björn Ommer |
| 2021 | Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations. Jiayao Zhang, Hua Wang, Weijie J. Su |
| 2021 | Imitation with Neural Density Models. Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon |
| 2021 | Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion |
| 2021 | Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth |
| 2021 | Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path. Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain, Haipeng Luo |
| 2021 | Implicit Generative Copulas. Tim Janke, Mohamed Ghanmi, Florian Steinke |
| 2021 | Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions. Mathias Niepert, Pasquale Minervini, Luca Franceschi |
| 2021 | Implicit Regularization in Matrix Sensing via Mirror Descent. Fan Wu, Patrick Rebeschini |
| 2021 | Implicit SVD for Graph Representation Learning. Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan |
| 2021 | Implicit Semantic Response Alignment for Partial Domain Adaptation. Wenxiao Xiao, Zhengming Ding, Hongfu Liu |
| 2021 | Implicit Sparse Regularization: The Impact of Depth and Early Stopping. Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Ka Wai Wong |
| 2021 | Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation. Mao Li, Kaiqi Jiang, Xinhua Zhang |
| 2021 | Implicit Transformer Network for Screen Content Image Continuous Super-Resolution. Jingyu Yang, Sheng Shen, Huanjing Yue, Kun Li |
| 2021 | Impression learning: Online representation learning with synaptic plasticity. Colin Bredenberg, Benjamin Lyo, Eero P. Simoncelli, Cristina Savin |
| 2021 | Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction. Gal Dalal, Assaf Hallak, Steven Dalton, Iuri Frosio, Shie Mannor, Gal Chechik |
| 2021 | Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn |
| 2021 | Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan |
| 2021 | Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu |
| 2021 | Improved Regret Bounds for Tracking Experts with Memory. James Robinson, Mark Herbster |
| 2021 | Improved Regularization and Robustness for Fine-tuning in Neural Networks. Dongyue Li, Hongyang R. Zhang |
| 2021 | Improved Transformer for High-Resolution GANs. Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang |
| 2021 | Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP. Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du |
| 2021 | Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss. Michael L. Iuzzolino, Michael C. Mozer, Samy Bengio |
| 2021 | Improving Calibration through the Relationship with Adversarial Robustness. Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi |
| 2021 | Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. Maxwell I. Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake |
| 2021 | Improving Compositionality of Neural Networks by Decoding Representations to Inputs. Mike Wu, Noah D. Goodman, Stefano Ermon |
| 2021 | Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings. Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel |
| 2021 | Improving Conditional Coverage via Orthogonal Quantile Regression. Shai Feldman, Stephen Bates, Yaniv Romano |
| 2021 | Improving Contrastive Learning on Imbalanced Data via Open-World Sampling. Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang |
| 2021 | Improving Deep Learning Interpretability by Saliency Guided Training. Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi |
| 2021 | Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture. Suyoung Lee, Sae-Young Chung |
| 2021 | Improving Robustness using Generated Data. Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy A. Mann |
| 2021 | Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration. Yu Wang, Jingyang Lin, Jingjing Zou, Yingwei Pan, Ting Yao, Tao Mei |
| 2021 | Improving Transferability of Representations via Augmentation-Aware Self-Supervision. Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin |
| 2021 | Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers. Yanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang, Jianlong Fu |
| 2021 | Improving black-box optimization in VAE latent space using decoder uncertainty. Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal |
| 2021 | Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity. Vladimir A. Ivanov, Konstantinos P. Michmizos |
| 2021 | Independent Prototype Propagation for Zero-Shot Compositionality. Frank Ruis, Gertjan J. Burghouts, Doina Bucur |
| 2021 | Independent mechanism analysis, a new concept? Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve |
| 2021 | Indexed Minimum Empirical Divergence for Unimodal Bandits. Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard |
| 2021 | Individual Privacy Accounting via a Rényi Filter. Vitaly Feldman, Tijana Zrnic |
| 2021 | Infinite Time Horizon Safety of Bayesian Neural Networks. Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger |
| 2021 | Influence Patterns for Explaining Information Flow in BERT. Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta |
| 2021 | InfoGCL: Information-Aware Graph Contrastive Learning. Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang |
| 2021 | Information Directed Reward Learning for Reinforcement Learning. David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause |
| 2021 | Information Directed Sampling for Sparse Linear Bandits. Botao Hao, Tor Lattimore, Wei Deng |
| 2021 | Information is Power: Intrinsic Control via Information Capture. Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine |
| 2021 | Information-constrained optimization: can adaptive processing of gradients help? Jayadev Acharya, Clément L. Canonne, Prathamesh Mayekar, Himanshu Tyagi |
| 2021 | Information-theoretic generalization bounds for black-box learning algorithms. Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan |
| 2021 | Instance-Conditional Knowledge Distillation for Object Detection. Zijian Kang, Peizhen Zhang, Xiangyu Zhang, Jian Sun, Nanning Zheng |
| 2021 | Instance-Conditioned GAN. Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michal Drozdzal, Adriana Romero-Soriano |
| 2021 | Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates. François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz |
| 2021 | Instance-Dependent Partial Label Learning. Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang |
| 2021 | Instance-dependent Label-noise Learning under a Structural Causal Model. Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang |
| 2021 | Instance-optimal Mean Estimation Under Differential Privacy. Ziyue Huang, Yuting Liang, Ke Yi |
| 2021 | Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression. Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul W. G. Elbers, Mihaela van der Schaar |
| 2021 | Integrating Tree Path in Transformer for Code Representation. Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, Zhi Jin |
| 2021 | Interactive Label Cleaning with Example-based Explanations. Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini |
| 2021 | Interesting Object, Curious Agent: Learning Task-Agnostic Exploration. Simone Parisi, Victoria Dean, Deepak Pathak, Abhinav Gupta |
| 2021 | Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning. Aakash Kaku, Sahana Upadhya, Narges Razavian |
| 2021 | Interpolation can hurt robust generalization even when there is no noise. Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang |
| 2021 | Interpretable agent communication from scratch (with a generic visual processor emerging on the side). Roberto Dessì, Eugene Kharitonov, Marco Baroni |
| 2021 | Interpreting Representation Quality of DNNs for 3D Point Cloud Processing. Wen Shen, Qihan Ren, Dongrui Liu, Quanshi Zhang |
| 2021 | Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting |
| 2021 | Intriguing Properties of Contrastive Losses. Ting Chen, Calvin Luo, Lala Li |
| 2021 | Intriguing Properties of Vision Transformers. Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang |
| 2021 | Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli |
| 2021 | Introspective Distillation for Robust Question Answering. Yulei Niu, Hanwang Zhang |
| 2021 | Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish |
| 2021 | Invariant Causal Imitation Learning for Generalizable Policies. Ioana Bica, Daniel Jarrett, Mihaela van der Schaar |
| 2021 | Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System. Matthias Schultheis, Dominik Straub, Constantin A. Rothkopf |
| 2021 | Inverse Problems Leveraging Pre-trained Contrastive Representations. Sriram Ravula, Georgios Smyrnis, Matt Jordan, Alexandros G. Dimakis |
| 2021 | Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees. Gregory Dexter, Kevin Bello, Jean Honorio |
| 2021 | Inverse-Weighted Survival Games. Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath |
| 2021 | Invertible DenseNets with Concatenated LipSwish. Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai |
| 2021 | Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis. Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho |
| 2021 | Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence. Alexander Miserlis Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan L. Boyd-Graber, Philip Resnik |
| 2021 | Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies. Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin A. Riedmiller, Markus Wulfmeier, Daniela Rus |
| 2021 | Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis. Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima |
| 2021 | It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems. Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin |
| 2021 | Iterative Amortized Policy Optimization. Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue |
| 2021 | Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias. Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik |
| 2021 | Iterative Connecting Probability Estimation for Networks. Yichen Qin, Linhan Yu, Yang Li |
| 2021 | Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. Terrance Liu, Giuseppe Vietri, Steven Wu |
| 2021 | Iterative Teacher-Aware Learning. Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu |
| 2021 | Iterative Teaching by Label Synthesis. Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller |
| 2021 | Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger |
| 2021 | Joint Inference for Neural Network Depth and Dropout Regularization. Kishan K. C., Rui Li, Mahdi Gilany |
| 2021 | Joint Modeling of Visual Objects and Relations for Scene Graph Generation. Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang |
| 2021 | Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection. Jingjing Li, Wei Ji, Qi Bi, Cheng Yan, Miao Zhang, Yongri Piao, Huchuan Lu, Li Cheng |
| 2021 | Joint inference and input optimization in equilibrium networks. Swaminathan Gurumurthy, Shaojie Bai, Zachary Manchester, J. Zico Kolter |
| 2021 | K-Net: Towards Unified Image Segmentation. Wenwei Zhang, Jiangmiao Pang, Kai Chen, Chen Change Loy |
| 2021 | K-level Reasoning for Zero-Shot Coordination in Hanabi. Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster |
| 2021 | KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. Pierre Glaser, Michael Arbel, Arthur Gretton |
| 2021 | KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network. Yu Hao, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang |
| 2021 | Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers. Mandela Patrick, Dylan Campbell, Yuki M. Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, João F. Henriques |
| 2021 | Kernel Functional Optimisation. Arun Kumar Anjanapura Venkatesh, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh |
| 2021 | Kernel Identification Through Transformers. Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen |
| 2021 | Kernelized Heterogeneous Risk Minimization. Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen |
| 2021 | Knowledge-Adaptation Priors. Mohammad Emtiyaz Khan, Siddharth Swaroop |
| 2021 | Knowledge-inspired 3D Scene Graph Prediction in Point Cloud. Shoulong Zhang, Shuai Li, Aimin Hao, Hong Qin |
| 2021 | L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization. Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan |
| 2021 | LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning. Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-Chul Moon |
| 2021 | LEADS: Learning Dynamical Systems that Generalize Across Environments. Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari |
| 2021 | LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi |
| 2021 | LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning. Chonghao Sima, Yexiang Xue |
| 2021 | Label Disentanglement in Partition-based Extreme Multilabel Classification. Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2021 | Label Noise SGD Provably Prefers Flat Global Minimizers. Alex Damian, Tengyu Ma, Jason D. Lee |
| 2021 | Label consistency in overfitted generalized $k$-means. Linfan Zhang, Arash A. Amini |
| 2021 | Label-Imbalanced and Group-Sensitive Classification under Overparameterization. Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis |
| 2021 | Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, Long Jin |
| 2021 | Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning. Junsu Kim, Younggyo Seo, Jinwoo Shin |
| 2021 | Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision. Keji He, Yan Huang, Qi Wu, Jianhua Yang, Dong An, Shuanglin Sima, Liang Wang |
| 2021 | Landscape analysis of an improved power method for tensor decomposition. Joe Kileel, Timo Klock, João M. Pereira |
| 2021 | Language models enable zero-shot prediction of the effects of mutations on protein function. Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, Alexander Rives |
| 2021 | Laplace Redux - Effortless Bayesian Deep Learning. Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig |
| 2021 | Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, Ser-Nam Lim |
| 2021 | Large-Scale Learning with Fourier Features and Tensor Decompositions. Frederiek Wesel, Kim Batselier |
| 2021 | Large-Scale Unsupervised Object Discovery. Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce |
| 2021 | Large-Scale Wasserstein Gradient Flows. Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M. Solomon, Evgeny Burnaev |
| 2021 | Last iterate convergence of SGD for Least-Squares in the Interpolation regime. Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion |
| 2021 | Last-iterate Convergence in Extensive-Form Games. Chung-wei Lee, Christian Kroer, Haipeng Luo |
| 2021 | Latent Equilibrium: Arbitrarily fast computation with arbitrarily slow neurons. Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici |
| 2021 | Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages. Xinyun Chen, Dawn Song, Yuandong Tian |
| 2021 | Latent Matters: Learning Deep State-Space Models. Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt |
| 2021 | Lattice partition recovery with dyadic CART. Oscar Hernan Madrid Padilla, Yi Yu, Alessandro Rinaldo |
| 2021 | Learnability of Linear Thresholds from Label Proportions. Rishi Saket |
| 2021 | Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding. Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio |
| 2021 | Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection. HanQin Cai, Jialin Liu, Wotao Yin |
| 2021 | Learning 3D Dense Correspondence via Canonical Point Autoencoder. An-Chieh Cheng, Xueting Li, Min Sun, Ming-Hsuan Yang, Sifei Liu |
| 2021 | Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations. Yuping Luo, Tengyu Ma |
| 2021 | Learning Causal Semantic Representation for Out-of-Distribution Prediction. Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu |
| 2021 | Learning Collaborative Policies to Solve NP-hard Routing Problems. Minsu Kim, Jinkyoo Park, Joungho Kim |
| 2021 | Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne |
| 2021 | Learning Conjoint Attentions for Graph Neural Nets. Tiantian He, Yew Soon Ong, Lu Bai |
| 2021 | Learning Debiased Representation via Disentangled Feature Augmentation. Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, Jaegul Choo |
| 2021 | Learning Debiased and Disentangled Representations for Semantic Segmentation. Sanghyeok Chu, Dongwan Kim, Bohyung Han |
| 2021 | Learning Disentangled Behavior Embeddings. Changhao Shi, Sivan Schwartz, Shahar Levy, Shay Achvat, Maisan Abboud, Amir Ghanayim, Jackie Schiller, Gal Mishne |
| 2021 | Learning Distilled Collaboration Graph for Multi-Agent Perception. Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, WenJun Zhang |
| 2021 | Learning Diverse Policies in MOBA Games via Macro-Goals. Yiming Gao, Bei Shi, Xueying Du, Liang Wang, Guangwei Chen, Zhenjie Lian, Fuhao Qiu, Guoan Han, Weixuan Wang, Deheng Ye, Qiang Fu, Wei Yang, Lanxiao Huang |
| 2021 | Learning Domain Invariant Representations in Goal-conditioned Block MDPs. Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba |
| 2021 | Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention. Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim |
| 2021 | Learning Equilibria in Matching Markets from Bandit Feedback. Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt |
| 2021 | Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. Priyank Jaini, Lars Holdijk, Max Welling |
| 2021 | Learning Fast-Inference Bayesian Networks. Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider |
| 2021 | Learning Frequency Domain Approximation for Binary Neural Networks. Yixing Xu, Kai Han, Chang Xu, Yehui Tang, Chunjing Xu, Yunhe Wang |
| 2021 | Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions. Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová |
| 2021 | Learning Generalized Gumbel-max Causal Mechanisms. Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan |
| 2021 | Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction. Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li |
| 2021 | Learning Graph Cellular Automata. Daniele Grattarola, Lorenzo Livi, Cesare Alippi |
| 2021 | Learning Graph Models for Retrosynthesis Prediction. Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay |
| 2021 | Learning Hard Optimization Problems: A Data Generation Perspective. James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck |
| 2021 | Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence. Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, Qi Tian, Junchi Yan |
| 2021 | Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach. Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sun |
| 2021 | Learning Knowledge Graph-based World Models of Textual Environments. Prithviraj Ammanabrolu, Mark O. Riedl |
| 2021 | Learning Large Neighborhood Search Policy for Integer Programming. Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang |
| 2021 | Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe |
| 2021 | Learning Markov State Abstractions for Deep Reinforcement Learning. Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris |
| 2021 | Learning Models for Actionable Recourse. Alexis Ross, Himabindu Lakkaraju, Osbert Bastani |
| 2021 | Learning Nonparametric Volterra Kernels with Gaussian Processes. Magnus Ross, Michael T. Smith, Mauricio A. Álvarez |
| 2021 | Learning One Representation to Optimize All Rewards. Ahmed Touati, Yann Ollivier |
| 2021 | Learning Optimal Predictive Checklists. Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi |
| 2021 | Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs. Tao Liu, Ruida Zhou, Dileep Kalathil, Panganamala R. Kumar, Chao Tian |
| 2021 | Learning Riemannian metric for disease progression modeling. Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley Durrleman |
| 2021 | Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies. Dushyant Sahoo, Christos Davatzikos |
| 2021 | Learning Semantic Representations to Verify Hardware Designs. Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, Richard Ho, Charles Sutton |
| 2021 | Learning Signal-Agnostic Manifolds of Neural Fields. Yilun Du, Katie Collins, Josh Tenenbaum, Vincent Sitzmann |
| 2021 | Learning Space Partitions for Path Planning. Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian |
| 2021 | Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems. Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler |
| 2021 | Learning State Representations from Random Deep Action-conditional Predictions. Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh |
| 2021 | Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj |
| 2021 | Learning Student-Friendly Teacher Networks for Knowledge Distillation. Dae Young Park, Moon-Hyun Cha, Changwook Jeong, Daesin Kim, Bohyung Han |
| 2021 | Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar |
| 2021 | Learning Transferable Adversarial Perturbations. Krishna Kanth Nakka, Mathieu Salzmann |
| 2021 | Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training. Yihan Zeng, Chunwei Wang, Yunbo Wang, Hang Xu, Chaoqiang Ye, Zhen Yang, Chao Ma |
| 2021 | Learning Treatment Effects in Panels with General Intervention Patterns. Vivek F. Farias, Andrew A. Li, Tianyi Peng |
| 2021 | Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart |
| 2021 | Learning a Single Neuron with Bias Using Gradient Descent. Gal Vardi, Gilad Yehudai, Ohad Shamir |
| 2021 | Learning and Generalization in RNNs. Abhishek Panigrahi, Navin Goyal |
| 2021 | Learning curves of generic features maps for realistic datasets with a teacher-student model. Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová |
| 2021 | Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering. Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, Nan Duan |
| 2021 | Learning in Multi-Stage Decentralized Matching Markets. Xiaowu Dai, Michael I. Jordan |
| 2021 | Learning in Non-Cooperative Configurable Markov Decision Processes. Giorgia Ramponi, Alberto Maria Metelli, Alessandro Concetti, Marcello Restelli |
| 2021 | Learning in two-player zero-sum partially observable Markov games with perfect recall. Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko |
| 2021 | Learning interaction rules from multi-animal trajectories via augmented behavioral models. Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara |
| 2021 | Learning latent causal graphs via mixture oracles. Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam |
| 2021 | Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters. Takanori Maehara, Hoang NT |
| 2021 | Learning rule influences recurrent network representations but not attractor structure in decision-making tasks. Brandon McMahan, Michael Kleinman, Jonathan C. Kao |
| 2021 | Learning the optimal Tikhonov regularizer for inverse problems. Giovanni S. Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria |
| 2021 | Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation. Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang |
| 2021 | Learning to Assimilate in Chaotic Dynamical Systems. Michael McCabe, Jed Brown |
| 2021 | Learning to Combine Per-Example Solutions for Neural Program Synthesis. Disha Shrivastava, Hugo Larochelle, Daniel Tarlow |
| 2021 | Learning to Compose Visual Relations. Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, Antonio Torralba |
| 2021 | Learning to Draw: Emergent Communication through Sketching. Daniela Mihai, Jonathon S. Hare |
| 2021 | Learning to Elect. Cem Anil, Xuchan Bao |
| 2021 | Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. Ingmar Schubert, Danny Driess, Ozgur S. Oguz, Marc Toussaint |
| 2021 | Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training. Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Donglai Wei |
| 2021 | Learning to Generate Visual Questions with Noisy Supervision. Kai Shen, Lingfei Wu, Siliang Tang, Yueting Zhuang, Zhen He, Zhuoye Ding, Yun Xiao, Bo Long |
| 2021 | Learning to Ground Multi-Agent Communication with Autoencoders. Toru Lin, Jacob Huh, Christopher Stauffer, Ser-Nam Lim, Phillip Isola |
| 2021 | Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer. Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang |
| 2021 | Learning to Learn Dense Gaussian Processes for Few-Shot Learning. Ze Wang, Zichen Miao, Xiantong Zhen, Qiang Qiu |
| 2021 | Learning to Learn Graph Topologies. Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen |
| 2021 | Learning to Predict Trustworthiness with Steep Slope Loss. Yan Luo, Yongkang Wong, Mohan S. Kankanhalli, Qi Zhao |
| 2021 | Learning to Schedule Heuristics in Branch and Bound. Antonia Chmiela, Elias B. Khalil, Ambros M. Gleixner, Andrea Lodi, Sebastian Pokutta |
| 2021 | Learning to See by Looking at Noise. Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba |
| 2021 | Learning to Select Exogenous Events for Marked Temporal Point Process. Ping Zhang, Rishabh K. Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De |
| 2021 | Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization. Zhenghao Peng, Quanyi Li, Ka-Ming Hui, Chunxiao Liu, Bolei Zhou |
| 2021 | Learning to Synthesize Programs as Interpretable and Generalizable Policies. Dweep Trivedi, Jesse Zhang, Shao-Hua Sun, Joseph J. Lim |
| 2021 | Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck. Nicolas Skatchkovsky, Osvaldo Simeone, Hyeryung Jang |
| 2021 | Learning to dehaze with polarization. Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi |
| 2021 | Learning to delegate for large-scale vehicle routing. Sirui Li, Zhongxia Yan, Cathy Wu |
| 2021 | Learning where to learn: Gradient sparsity in meta and continual learning. Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento |
| 2021 | Learning with Algorithmic Supervision via Continuous Relaxations. Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen |
| 2021 | Learning with Holographic Reduced Representations. Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean |
| 2021 | Learning with Labeling Induced Abstentions. Kareem Amin, Giulia DeSalvo, Afshin Rostamizadeh |
| 2021 | Learning with Noisy Correspondence for Cross-modal Matching. Zhenyu Huang, Guocheng Niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng |
| 2021 | Learning with User-Level Privacy. Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh |
| 2021 | Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon |
| 2021 | Learning-to-learn non-convex piecewise-Lipschitz functions. Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar |
| 2021 | Least Square Calibration for Peer Reviews. Sijun Tan, Jibang Wu, Xiaohui Bei, Haifeng Xu |
| 2021 | Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation. Weiming Liu, Jiajie Su, Chaochao Chen, Xiaolin Zheng |
| 2021 | Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P. Vetrov |
| 2021 | Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds. Xiaolong Li, Yijia Weng, Li Yi, Leonidas J. Guibas, A. Lynn Abbott, Shuran Song, He Wang |
| 2021 | Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation. Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi |
| 2021 | Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning. Christopher Michael Rytting, David Wingate |
| 2021 | Lifelong Domain Adaptation via Consolidated Internal Distribution. Mohammad Rostami |
| 2021 | Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering. Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, Frédo Durand |
| 2021 | Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training. Huy Tuan Pham, Phan-Minh Nguyen |
| 2021 | Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. Aritra Mitra, Rayana H. Jaafar, George J. Pappas, Hamed Hassani |
| 2021 | Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models. Xiao Lv, Wei Cui, Yulong Liu |
| 2021 | Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters. Arvind V. Mahankali, David P. Woodruff |
| 2021 | Linear-Time Probabilistic Solution of Boundary Value Problems. Nicholas Krämer, Philipp Hennig |
| 2021 | Lip to Speech Synthesis with Visual Context Attentional GAN. Minsu Kim, Joanna Hong, Yong Man Ro |
| 2021 | List-Decodable Mean Estimation in Nearly-PCA Time. Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian |
| 2021 | Littlestone Classes are Privately Online Learnable. Noah Golowich, Roi Livni |
| 2021 | Local Differential Privacy for Regret Minimization in Reinforcement Learning. Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta |
| 2021 | Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization. Travers Rhodes, Daniel D. Lee |
| 2021 | Local Explanation of Dialogue Response Generation. Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang |
| 2021 | Local Hyper-Flow Diffusion. Kimon Fountoulakis, Pan Li, Shenghao Yang |
| 2021 | Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels. Stefani Karp, Ezra Winston, Yuanzhi Li, Aarti Singh |
| 2021 | Local plasticity rules can learn deep representations using self-supervised contrastive predictions. Bernd Illing, Jean Ventura, Guillaume Bellec, Wulfram Gerstner |
| 2021 | Local policy search with Bayesian optimization. Sarah Müller, Alexander von Rohr, Sebastian Trimpe |
| 2021 | Locality Sensitive Teaching. Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava |
| 2021 | Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. Alessandro Favero, Francesco Cagnetta, Matthieu Wyart |
| 2021 | Localization with Sampling-Argmax. Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu |
| 2021 | Localization, Convexity, and Star Aggregation. Suhas Vijaykumar |
| 2021 | Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models. Keunseo Kim, Juncheol Shin, Heeyoung Kim |
| 2021 | Locally Valid and Discriminative Prediction Intervals for Deep Learning Models. Zhen Lin, Shubhendu Trivedi, Jimeng Sun |
| 2021 | Locally differentially private estimation of functionals of discrete distributions. Cristina Butucea, Yann Issartel |
| 2021 | Locally private online change point detection. Thomas Berrett, Yi Yu |
| 2021 | Logarithmic Regret from Sublinear Hints. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit |
| 2021 | Logarithmic Regret in Feature-based Dynamic Pricing. Jianyu Xu, Yu-Xiang Wang |
| 2021 | Long Short-Term Transformer for Online Action Detection. Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia, Zhuowen Tu, Stefano Soatto |
| 2021 | Long-Short Transformer: Efficient Transformers for Language and Vision. Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro |
| 2021 | Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos. Reuben Tan, Bryan A. Plummer, Kate Saenko, Hailin Jin, Bryan C. Russell |
| 2021 | Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis. Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre |
| 2021 | Looking Beyond Single Images for Contrastive Semantic Segmentation Learning. Feihu Zhang, Philip H. S. Torr, René Ranftl, Stephan R. Richter |
| 2021 | Loss function based second-order Jensen inequality and its application to particle variational inference. Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama |
| 2021 | Lossy Compression for Lossless Prediction. Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison |
| 2021 | Low-Fidelity Video Encoder Optimization for Temporal Action Localization. Mengmeng Xu, Juan-Manuel Pérez-Rúa, Xiatian Zhu, Bernard Ghanem, Brais Martínez |
| 2021 | Low-Rank Constraints for Fast Inference in Structured Models. Justin T. Chiu, Yuntian Deng, Alexander M. Rush |
| 2021 | Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. Atara Kaplan, Dan Garber |
| 2021 | Low-Rank Subspaces in GANs. Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, Zheng-Jun Zha, Jingren Zhou, Qifeng Chen |
| 2021 | Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses. Richard J. Antonello, Javier S. Turek, Vy Ai Vo, Alexander Huth |
| 2021 | Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks. Dmitry Kovalev, Elnur Gasanov, Alexander V. Gasnikov, Peter Richtárik |
| 2021 | Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. Yin Tat Lee, Ruoqi Shen, Kevin Tian |
| 2021 | Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models. Behnoush Khavari, Guillaume Rabusseau |
| 2021 | Luna: Linear Unified Nested Attention. Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, Luke Zettlemoyer |
| 2021 | M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. Elias Frantar, Eldar Kurtic, Dan Alistarh |
| 2021 | MADE: Exploration via Maximizing Deviation from Explored Regions. Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E. Gonzalez, Stuart Russell |
| 2021 | MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents. Stephen Chung |
| 2021 | MAU: A Motion-Aware Unit for Video Prediction and Beyond. Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xiang Xinguang, Wen Gao |
| 2021 | MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaïd Harchaoui |
| 2021 | MCMC Variational Inference via Uncorrected Hamiltonian Annealing. Tomas Geffner, Justin Domke |
| 2021 | MERLOT: Multimodal Neural Script Knowledge Models. Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi |
| 2021 | MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge. Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin |
| 2021 | MICo: Improved representations via sampling-based state similarity for Markov decision processes. Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland |
| 2021 | MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar |
| 2021 | MLP-Mixer: An all-MLP Architecture for Vision. Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy |
| 2021 | MOMA: Multi-Object Multi-Actor Activity Parsing. Zelun Luo, Wanze Xie, Siddharth Kapoor, Yiyun Liang, Michael Cooper, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li |
| 2021 | MST: Masked Self-Supervised Transformer for Visual Representation. Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu, Chaoyang Zhao, Rui Deng, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang |
| 2021 | Machine Learning for Variance Reduction in Online Experiments. Yongyi Guo, Dominic Coey, Mikael Konutgan, Wenting Li, Chris Schoener, Matt Goldman |
| 2021 | Machine learning structure preserving brackets for forecasting irreversible processes. Kookjin Lee, Nathaniel Trask, Panos Stinis |
| 2021 | Machine versus Human Attention in Deep Reinforcement Learning Tasks. Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Dana H. Ballard, Mary M. Hayhoe, Peter Stone |
| 2021 | MagNet: A Neural Network for Directed Graphs. Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew J. Hirn |
| 2021 | Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar |
| 2021 | Making a (Counterfactual) Difference One Rationale at a Time. Mitchell Plyler, Michael Green, Min Chi |
| 2021 | Making the most of your day: online learning for optimal allocation of time. Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini |
| 2021 | Manifold Topology Divergence: a Framework for Comparing Data Manifolds. Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev |
| 2021 | Manipulating SGD with Data Ordering Attacks. Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson |
| 2021 | Margin-Independent Online Multiclass Learning via Convex Geometry. Guru Guruganesh, Allen Liu, Jon Schneider, Joshua R. Wang |
| 2021 | Marginalised Gaussian Processes with Nested Sampling. Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen |
| 2021 | MarioNette: Self-Supervised Sprite Learning. Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin M. Solomon |
| 2021 | Mastering Atari Games with Limited Data. Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao |
| 2021 | Matching a Desired Causal State via Shift Interventions. Jiaqi Zhang, Chandler Squires, Caroline Uhler |
| 2021 | Matrix encoding networks for neural combinatorial optimization. Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon, Minah Park, Duwon Park, Youngjune Gwon |
| 2021 | Matrix factorisation and the interpretation of geodesic distance. Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy |
| 2021 | Maximum Likelihood Training of Score-Based Diffusion Models. Yang Song, Conor Durkan, Iain Murray, Stefano Ermon |
| 2021 | Measuring Generalization with Optimal Transport. Ching-Yao Chuang, Youssef Mroueh, Kristjan H. Greenewald, Antonio Torralba, Stefanie Jegelka |
| 2021 | Medical Dead-ends and Learning to Identify High-Risk States and Treatments. Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi |
| 2021 | Memory Efficient Meta-Learning with Large Images. John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner |
| 2021 | Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering. Nimita Shinde, Vishnu Narayanan, James Saunderson |
| 2021 | Memory-efficient Patch-based Inference for Tiny Deep Learning. Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han |
| 2021 | Meta Internal Learning. Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf |
| 2021 | Meta Learning Backpropagation And Improving It. Louis Kirsch, Jürgen Schmidhuber |
| 2021 | Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland |
| 2021 | Meta-Adaptive Nonlinear Control: Theory and Algorithms. Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue |
| 2021 | Meta-Learning Reliable Priors in the Function Space. Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause |
| 2021 | Meta-Learning Sparse Implicit Neural Representations. Jaeho Lee, Jihoon Tack, Namhoon Lee, Jinwoo Shin |
| 2021 | Meta-Learning for Relative Density-Ratio Estimation. Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara |
| 2021 | Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein |
| 2021 | Meta-Learning via Learning with Distributed Memory. Sudarshan Babu, Pedro Savarese, Michael Maire |
| 2021 | Meta-learning to Improve Pre-training. Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud |
| 2021 | Meta-learning with an Adaptive Task Scheduler. Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn |
| 2021 | MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images. Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang |
| 2021 | Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models. Runzhe Wan, Lin Ge, Rui Song |
| 2021 | Metropolis-Hastings Data Augmentation for Graph Neural Networks. Hyeon-Jin Park, Seunghun Lee, Sihyeon Kim, Jinyoung Park, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim |
| 2021 | Mind the Gap: Assessing Temporal Generalization in Neural Language Models. Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d'Autume, Tomás Kociský, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom |
| 2021 | Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding. Andreis Bruno, Jeffrey Willette, Juho Lee, Sung Ju Hwang |
| 2021 | Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization. Qi Deng, Wenzhi Gao |
| 2021 | Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy |
| 2021 | Minimax Regret for Stochastic Shortest Path. Alon Cohen, Yonathan Efroni, Yishay Mansour, Aviv Rosenberg |
| 2021 | Minimizing Polarization and Disagreement in Social Networks via Link Recommendation. Liwang Zhu, Qi Bao, Zhongzhi Zhang |
| 2021 | Mining the Benefits of Two-stage and One-stage HOI Detection. Aixi Zhang, Yue Liao, Si Liu, Miao Lu, Yongliang Wang, Chen Gao, Xiaobo Li |
| 2021 | Mirror Langevin Monte Carlo: the Case Under Isoperimetry. Qijia Jiang |
| 2021 | Misspecified Gaussian Process Bandit Optimization. Ilija Bogunovic, Andreas Krause |
| 2021 | Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun |
| 2021 | Mitigating Forgetting in Online Continual Learning with Neuron Calibration. Haiyan Yin, Peng Yang, Ping Li |
| 2021 | MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps. Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li |
| 2021 | MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data. Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou |
| 2021 | Mixability made efficient: Fast online multiclass logistic regression. Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi |
| 2021 | Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity. Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, Liqing Zhang |
| 2021 | Mixture Proportion Estimation and PU Learning: A Modern Approach. Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton |
| 2021 | Mixture weights optimisation for Alpha-Divergence Variational Inference. Kamélia Daudel, Randal Douc |
| 2021 | MobILE: Model-Based Imitation Learning From Observation Alone. Rahul Kidambi, Jonathan D. Chang, Wen Sun |
| 2021 | MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction. Hao Xue, Flora D. Salim, Yongli Ren, Nuria Oliver |
| 2021 | Modality-Agnostic Topology Aware Localization. Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli |
| 2021 | Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data. Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu |
| 2021 | Model Selection for Bayesian Autoencoders. Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone |
| 2021 | Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model. Antoine Bodin, Nicolas Macris |
| 2021 | Model-Based Domain Generalization. Alexander Robey, George J. Pappas, Hamed Hassani |
| 2021 | Model-Based Episodic Memory Induces Dynamic Hybrid Controls. Hung Le, Thommen George Karimpanal, Majid Abdolshah, Truyen Tran, Svetha Venkatesh |
| 2021 | Model-Based Reinforcement Learning via Imagination with Derived Memory. Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao |
| 2021 | Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones. Yushi Bai, Zhitao Ying, Hongyu Ren, Jure Leskovec |
| 2021 | Modified Frank Wolfe in Probability Space. Carson Kent, Jiajin Li, José H. Blanchet, Peter W. Glynn |
| 2021 | Modular Gaussian Processes for Transfer Learning. Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez |
| 2021 | Momentum Centering and Asynchronous Update for Adaptive Gradient Methods. Juntang Zhuang, Yifan Ding, Tommy Tang, Nicha C. Dvornek, Sekhar Tatikonda, James S. Duncan |
| 2021 | Monte Carlo Tree Search With Iteratively Refining State Abstractions. Samuel Sokota, Caleb Ho, Zaheen Farraz Ahmad, J. Zico Kolter |
| 2021 | Morié Attack (MA): A New Potential Risk of Screen Photos. Dantong Niu, Ruohao Guo, Yisen Wang |
| 2021 | Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data. Gongfan Fang, Yifan Bao, Jie Song, Xinchao Wang, Donglin Xie, Chengchao Shen, Mingli Song |
| 2021 | Moser Flow: Divergence-based Generative Modeling on Manifolds. Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman |
| 2021 | Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices. Max Ryabinin, Eduard Gorbunov, Vsevolod Plokhotnyuk, Gennady Pekhimenko |
| 2021 | Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee |
| 2021 | Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks. Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C. Green |
| 2021 | Multi-Agent Reinforcement Learning in Stochastic Networked Systems. Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman |
| 2021 | Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization. Arnab Maiti, Vishakha Patil, Arindam Khan |
| 2021 | Multi-Facet Clustering Variational Autoencoders. Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Chris C. Holmes |
| 2021 | Multi-Label Learning with Pairwise Relevance Ordering. Ming-Kun Xie, Sheng-Jun Huang |
| 2021 | Multi-Objective Meta Learning. Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang |
| 2021 | Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs. Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche |
| 2021 | Multi-Person 3D Motion Prediction with Multi-Range Transformers. Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang |
| 2021 | Multi-Scale Representation Learning on Proteins. Vignesh Ram Somnath, Charlotte Bunne, Andreas Krause |
| 2021 | Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier |
| 2021 | Multi-View Representation Learning via Total Correlation Objective. HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim |
| 2021 | Multi-armed Bandit Requiring Monotone Arm Sequences. Ningyuan Chen |
| 2021 | Multi-modal Dependency Tree for Video Captioning. Wentian Zhao, Xinxiao Wu, Jiebo Luo |
| 2021 | Multi-task Learning of Order-Consistent Causal Graphs. Xinshi Chen, Haoran Sun, Caleb Ellington, Eric P. Xing, Le Song |
| 2021 | Multi-view Contrastive Graph Clustering. Erlin Pan, Zhao Kang |
| 2021 | Multiclass Boosting and the Cost of Weak Learning. Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire |
| 2021 | Multiclass versus Binary Differentially Private PAC Learning. Satchit Sivakumar, Mark Bun, Marco Gaboardi |
| 2021 | Multilingual Pre-training with Universal Dependency Learning. Kailai Sun, Zuchao Li, Hai Zhao |
| 2021 | Multimodal Few-Shot Learning with Frozen Language Models. Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill |
| 2021 | Multimodal Virtual Point 3D Detection. Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl |
| 2021 | Multimodal and Multilingual Embeddings for Large-Scale Speech Mining. Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk |
| 2021 | Multiple Descent: Design Your Own Generalization Curve. Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi |
| 2021 | Multiwavelet-based Operator Learning for Differential Equations. Gaurav Gupta, Xiongye Xiao, Paul Bogdan |
| 2021 | NAS-Bench-x11 and the Power of Learning Curves. Shen Yan, Colin White, Yash Savani, Frank Hutter |
| 2021 | NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X. Robert |
| 2021 | NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs. Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang |
| 2021 | NORESQA: A Framework for Speech Quality Assessment using Non-Matching References. Pranay Manocha, Buye Xu, Anurag Kumar |
| 2021 | NTopo: Mesh-free Topology Optimization using Implicit Neural Representations. Jonas Zehnder, Yue Li, Stelian Coros, Bernhard Thomaszewski |
| 2021 | Natural continual learning: success is a journey, not (just) a destination. Ta-Chu Kao, Kristopher T. Jensen, Gido van de Ven, Alberto Bernacchia, Guillaume Hennequin |
| 2021 | Navigating to the Best Policy in Markov Decision Processes. Aymen Al Marjani, Aurélien Garivier, Alexandre Proutière |
| 2021 | NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild. Jason Y. Zhang, Gengshan Yang, Shubham Tulsiani, Deva Ramanan |
| 2021 | NeRV: Neural Representations for Videos. Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava |
| 2021 | Near Optimal Policy Optimization via REPS. Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum |
| 2021 | Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif |
| 2021 | Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning. Scott Sussex, Caroline Uhler, Andreas Krause |
| 2021 | Near-Optimal No-Regret Learning in General Games. Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich |
| 2021 | Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. Ming Yin, Yu Bai, Yu-Xiang Wang |
| 2021 | Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli |
| 2021 | Nearly Horizon-Free Offline Reinforcement Learning. Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi |
| 2021 | Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | Nearly-Tight and Oblivious Algorithms for Explainable Clustering. Buddhima Gamlath, Xinrui Jia, Adam Polak, Ola Svensson |
| 2021 | Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. Jakob Runge |
| 2021 | Neighborhood Reconstructing Autoencoders. Yonghyeon Lee, Hyeokjun Kwon, Frank C. Park |
| 2021 | Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction. Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J. Kim |
| 2021 | Nested Counterfactual Identification from Arbitrary Surrogate Experiments. Juan D. Correa, Sanghack Lee, Elias Bareinboim |
| 2021 | Nested Graph Neural Networks. Muhan Zhang, Pan Li |
| 2021 | Nested Variational Inference. Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent |
| 2021 | Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization. Rohan Ghosh, Mehul Motani |
| 2021 | NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction. Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, Wenping Wang |
| 2021 | NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL. Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai |
| 2021 | Neural Active Learning with Performance Guarantees. Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile |
| 2021 | Neural Additive Models: Interpretable Machine Learning with Neural Nets. Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton |
| 2021 | Neural Algorithmic Reasoners are Implicit Planners. Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic |
| 2021 | Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations. Hyeong-Seok Choi, Juheon Lee, WanSoo Kim, Jie Lee, Hoon Heo, Kyogu Lee |
| 2021 | Neural Architecture Dilation for Adversarial Robustness. Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu |
| 2021 | Neural Auto-Curricula in Two-Player Zero-Sum Games. Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen McAleer, Ying Wen, Jun Wang, Yaodong Yang |
| 2021 | Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, Jian Tang |
| 2021 | Neural Bootstrapper. Minsuk Shin, Hyungjoo Cho, Hyun-seok Min, Sungbin Lim |
| 2021 | Neural Circuit Synthesis from Specification Patterns. Frederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner |
| 2021 | Neural Distance Embeddings for Biological Sequences. Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Velickovic, Jure Leskovec, Pietro Liò |
| 2021 | Neural Dubber: Dubbing for Videos According to Scripts. Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, Hang Zhao |
| 2021 | Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh |
| 2021 | Neural Flows: Efficient Alternative to Neural ODEs. Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann |
| 2021 | Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering. Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs |
| 2021 | Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions. Michael Poli, Stefano Massaroli, Luca Scimeca, Sanghyuk Chun, Seong Joon Oh, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg |
| 2021 | Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung |
| 2021 | Neural Production Systems. Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio |
| 2021 | Neural Program Generation Modulo Static Analysis. Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Christopher M. Jermaine |
| 2021 | Neural Pseudo-Label Optimism for the Bank Loan Problem. Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster |
| 2021 | Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex. Colin Conwell, David Mayo, Andrei Barbu, Michael A. Buice, George Alvarez, Boris Katz |
| 2021 | Neural Relightable Participating Media Rendering. Quan Zheng, Gurprit Singh, Hans-Peter Seidel |
| 2021 | Neural Routing by Memory. Kaipeng Zhang, Zhenqiang Li, Zhifeng Li, Wei Liu, Yoichi Sato |
| 2021 | Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation. Yufei Wang, Can Xu, Huang Hu, Chongyang Tao, Stephen Wan, Mark Dras, Mark Johnson, Daxin Jiang |
| 2021 | Neural Scene Flow Prior. Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey |
| 2021 | Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems. Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi |
| 2021 | Neural Tangent Kernel Maximum Mean Discrepancy. Xiuyuan Cheng, Yao Xie |
| 2021 | Neural Trees for Learning on Graphs. Rajat Talak, Siyi Hu, Lisa R. Peng, Luca Carlone |
| 2021 | Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose. Angtian Wang, Shenxiao Mei, Alan L. Yuille, Adam Kortylewski |
| 2021 | Neural optimal feedback control with local learning rules. Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii |
| 2021 | Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition. Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan T. Barron, Hendrik P. A. Lensch |
| 2021 | NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem. Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang |
| 2021 | NeuroMLR: Robust & Reliable Route Recommendation on Road Networks. Jayant Jain, Vrittika Bagadia, Sahil Manchanda, Sayan Ranu |
| 2021 | Never Go Full Batch (in Stochastic Convex Optimization). Idan Amir, Yair Carmon, Tomer Koren, Roi Livni |
| 2021 | Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney |
| 2021 | No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, Jiashi Feng |
| 2021 | No RL, No Simulation: Learning to Navigate without Navigating. Meera Hahn, Devendra Singh Chaplot, Shubham Tulsiani, Mustafa Mukadam, James M. Rehg, Abhinav Gupta |
| 2021 | No Regrets for Learning the Prior in Bandits. Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári |
| 2021 | No-Press Diplomacy from Scratch. Anton Bakhtin, David J. Wu, Adam Lerer, Noam Brown |
| 2021 | No-regret Online Learning over Riemannian Manifolds. Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi |
| 2021 | Node Dependent Local Smoothing for Scalable Graph Learning. Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui |
| 2021 | Noether Networks: meta-learning useful conserved quantities. Ferran Alet, Dylan Doblar, Allan Zhou, Josh Tenenbaum, Kenji Kawaguchi, Chelsea Finn |
| 2021 | Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks. Hidenori Tanaka, Daniel Kunin |
| 2021 | Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images. Kwanyoung Kim, Jong Chul Ye |
| 2021 | Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks. Xingsi Dong, Tianhao Chu, Tiejun Huang, Zilong Ji, Si Wu |
| 2021 | Noisy Recurrent Neural Networks. Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation. Yue Wang, Shaofeng Zou, Yi Zhou |
| 2021 | Non-Gaussian Gaussian Processes for Few-Shot Regression. Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Tomasz Trzcinski, Przemyslaw Spurek, Maciej Zieba |
| 2021 | Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm. Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda |
| 2021 | Non-asymptotic Error Bounds for Bidirectional GANs. Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang |
| 2021 | Non-asymptotic convergence bounds for Wasserstein approximation using point clouds. Quentin Mérigot, Filippo Santambrogio, Clément Sarrazin |
| 2021 | Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis. Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang |
| 2021 | Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation. Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, Venkatesh Babu R. |
| 2021 | Nonparametric estimation of continuous DPPs with kernel methods. Michaël Fanuel, Rémi Bardenet |
| 2021 | Nonsmooth Implicit Differentiation for Machine-Learning and Optimization. Jérôme Bolte, Tam Le, Edouard Pauwels, Antonio Silveti-Falls |
| 2021 | Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data. HaiYing Wang, Aonan Zhang, Chong Wang |
| 2021 | Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition. Yulin Wang, Rui Huang, Shiji Song, Zeyi Huang, Gao Huang |
| 2021 | Not All Low-Pass Filters are Robust in Graph Convolutional Networks. Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu |
| 2021 | Novel Upper Bounds for the Constrained Most Probable Explanation Task. Tahrima Rahman, Sara Rouhani, Vibhav Gogate |
| 2021 | Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation. Bingchen Zhao, Kai Han |
| 2021 | NovelD: A Simple yet Effective Exploration Criterion. Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian |
| 2021 | Numerical Composition of Differential Privacy. Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz |
| 2021 | Numerical influence of ReLU'(0) on backpropagation. David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels |
| 2021 | NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM. Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu |
| 2021 | OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression. Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li |
| 2021 | Object DGCNN: 3D Object Detection using Dynamic Graphs. Yue Wang, Justin M. Solomon |
| 2021 | Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu |
| 2021 | Object-Centric Representation Learning with Generative Spatial-Temporal Factorization. Nanbo Li, Muhammad Ahmed Raza, Wenbin Hu, Zhaole Sun, Robert B. Fisher |
| 2021 | Object-aware Contrastive Learning for Debiased Scene Representation. Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin |
| 2021 | Observation-Free Attacks on Stochastic Bandits. Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy |
| 2021 | OctField: Hierarchical Implicit Functions for 3D Modeling. Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, Lin Gao |
| 2021 | Off-Policy Risk Assessment in Contextual Bandits. Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli |
| 2021 | Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration. Runzhe Wu, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang |
| 2021 | Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies. Ron Dorfman, Idan Shenfeld, Aviv Tamar |
| 2021 | Offline Model-based Adaptable Policy Learning. Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei (Tony) Qin, Wenjie Shang, Jieping Ye |
| 2021 | Offline RL Without Off-Policy Evaluation. David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna |
| 2021 | Offline Reinforcement Learning as One Big Sequence Modeling Problem. Michael Janner, Qiyang Li, Sergey Levine |
| 2021 | Offline Reinforcement Learning with Reverse Model-based Imagination. Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li, Chongjie Zhang |
| 2021 | On Blame Attribution for Accountable Multi-Agent Sequential Decision Making. Stelios Triantafyllou, Adish Singla, Goran Radanovic |
| 2021 | On Calibration and Out-of-Domain Generalization. Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit |
| 2021 | On Component Interactions in Two-Stage Recommender Systems. Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus |
| 2021 | On Contrastive Representations of Stochastic Processes. Emile Mathieu, Adam Foster, Yee Whye Teh |
| 2021 | On Density Estimation with Diffusion Models. Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho |
| 2021 | On Effective Scheduling of Model-based Reinforcement Learning. Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li |
| 2021 | On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu |
| 2021 | On Episodes, Prototypical Networks, and Few-Shot Learning. Steinar Laenen, Luca Bertinetto |
| 2021 | On Inductive Biases for Heterogeneous Treatment Effect Estimation. Alicia Curth, Mihaela van der Schaar |
| 2021 | On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness. Eric Mintun, Alexander Kirillov, Saining Xie |
| 2021 | On Joint Learning for Solving Placement and Routing in Chip Design. Ruoyu Cheng, Junchi Yan |
| 2021 | On Large-Cohort Training for Federated Learning. Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith |
| 2021 | On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Q. Phung |
| 2021 | On Linear Stability of SGD and Input-Smoothness of Neural Networks. Chao Ma, Lexing Ying |
| 2021 | On Locality of Local Explanation Models. Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C. Holmes |
| 2021 | On Margin-Based Cluster Recovery with Oracle Queries. Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice |
| 2021 | On Memorization in Probabilistic Deep Generative Models. Gerrit J. J. van den Burg, Christopher K. I. Williams |
| 2021 | On Model Calibration for Long-Tailed Object Detection and Instance Segmentation. Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao |
| 2021 | On Optimal Interpolation in Linear Regression. Eduard Oravkin, Patrick Rebeschini |
| 2021 | On Optimal Robustness to Adversarial Corruption in Online Decision Problems. Shinji Ito |
| 2021 | On Path Integration of Grid Cells: Group Representation and Isotropic Scaling. Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu |
| 2021 | On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh |
| 2021 | On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning. Julian G. Zilly, Alessandro Achille, Andrea Censi, Emilio Frazzoli |
| 2021 | On Provable Benefits of Depth in Training Graph Convolutional Networks. Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi |
| 2021 | On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao |
| 2021 | On Robust Optimal Transport: Computational Complexity and Barycenter Computation. Khang Le, Huy Nguyen, Quang Minh Nguyen, Tung Pham, Hung Bui, Nhat Ho |
| 2021 | On Success and Simplicity: A Second Look at Transferable Targeted Attacks. Zhengyu Zhao, Zhuoran Liu, Martha A. Larson |
| 2021 | On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons. Vishnu Veerathu, Arun Rajkumar |
| 2021 | On Training Implicit Models. Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin |
| 2021 | On UMAP's True Loss Function. Sebastian Damrich, Fred A. Hamprecht |
| 2021 | On learning sparse vectors from mixture of responses. Nikita Polyanskii |
| 2021 | On sensitivity of meta-learning to support data. Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun |
| 2021 | On the Algorithmic Stability of Adversarial Training. Yue Xing, Qifan Song, Guang Cheng |
| 2021 | On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. Yifan Hu, Xin Chen, Niao He |
| 2021 | On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar |
| 2021 | On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang |
| 2021 | On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms. Shuyu Cheng, Guoqiang Wu, Jun Zhu |
| 2021 | On the Convergence of Step Decay Step-Size for Stochastic Optimization. Xiaoyu Wang, Sindri Magnússon, Mikael Johansson |
| 2021 | On the Cryptographic Hardness of Learning Single Periodic Neurons. Min Jae Song, Ilias Zadik, Joan Bruna |
| 2021 | On the Equivalence between Neural Network and Support Vector Machine. Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng |
| 2021 | On the Estimation Bias in Double Q-Learning. Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang |
| 2021 | On the Existence of The Adversarial Bayes Classifier. Pranjal Awasthi, Natalie Frank, Mehryar Mohri |
| 2021 | On the Expected Complexity of Maxout Networks. Hanna Tseran, Guido Montúfar |
| 2021 | On the Expressivity of Markov Reward. David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh |
| 2021 | On the Frequency Bias of Generative Models. Katja Schwarz, Yiyi Liao, Andreas Geiger |
| 2021 | On the Generative Utility of Cyclic Conditionals. Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu |
| 2021 | On the Importance of Gradients for Detecting Distributional Shifts in the Wild. Rui Huang, Andrew Geng, Yixuan Li |
| 2021 | On the Out-of-distribution Generalization of Probabilistic Image Modelling. Mingtian Zhang, Andi Zhang, Steven McDonagh |
| 2021 | On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P. Vetrov |
| 2021 | On the Power of Differentiable Learning versus PAC and SQ Learning. Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro |
| 2021 | On the Power of Edge Independent Graph Models. Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis |
| 2021 | On the Provable Generalization of Recurrent Neural Networks. Lifu Wang, Bo Shen, Bo Hu, Xing Cao |
| 2021 | On the Representation Power of Set Pooling Networks. Christian Bueno, Alan Hylton |
| 2021 | On the Representation of Solutions to Elliptic PDEs in Barron Spaces. Ziang Chen, Jianfeng Lu, Yulong Lu |
| 2021 | On the Role of Optimization in Double Descent: A Least Squares Study. Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu |
| 2021 | On the Sample Complexity of Learning under Geometric Stability. Alberto Bietti, Luca Venturi, Joan Bruna |
| 2021 | On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. Menachem Sadigurschi, Uri Stemmer |
| 2021 | On the Second-order Convergence Properties of Random Search Methods. Aurélien Lucchi, Antonio Orvieto, Adamos Solomou |
| 2021 | On the Stochastic Stability of Deep Markov Models. Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar |
| 2021 | On the Suboptimality of Thompson Sampling in High Dimensions. Raymond Zhang, Richard Combes |
| 2021 | On the Theory of Reinforcement Learning with Once-per-Episode Feedback. Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2021 | On the Universality of Graph Neural Networks on Large Random Graphs. Nicolas Keriven, Alberto Bietti, Samuel Vaiter |
| 2021 | On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs). Zhiyuan Li, Sadhika Malladi, Sanjeev Arora |
| 2021 | On the Value of Infinite Gradients in Variational Autoencoder Models. Bin Dai, Wenliang Li, David P. Wipf |
| 2021 | On the Value of Interaction and Function Approximation in Imitation Learning. Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran |
| 2021 | On the Variance of the Fisher Information for Deep Learning. Alexander Soen, Ke Sun |
| 2021 | On the interplay between data structure and loss function in classification problems. Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli |
| 2021 | One Explanation is Not Enough: Structured Attention Graphs for Image Classification. Vivswan Shitole, Fuxin Li, Minsuk Kahng, Prasad Tadepalli, Alan Fern |
| 2021 | One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective. Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, Tao Xiang |
| 2021 | One More Step Towards Reality: Cooperative Bandits with Imperfect Communication. Udari Madhushani, Abhimanyu Dubey, Naomi Ehrich Leonard, Alex Pentland |
| 2021 | One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval. Akari Asai, Xinyan Yu, Jungo Kasai, Hanna Hajishirzi |
| 2021 | Online Active Learning with Surrogate Loss Functions. Giulia DeSalvo, Claudio Gentile, Tobias Sommer Thune |
| 2021 | Online Adaptation to Label Distribution Shift. Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger |
| 2021 | Online Control of Unknown Time-Varying Dynamical Systems. Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan |
| 2021 | Online Convex Optimization with Continuous Switching Constraint. Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang |
| 2021 | Online Facility Location with Multiple Advice. Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re |
| 2021 | Online Knapsack with Frequency Predictions. Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit |
| 2021 | Online Learning Of Neural Computations From Sparse Temporal Feedback. Mikio Ludwig Braun, Tim P. Vogels |
| 2021 | Online Learning and Control of Complex Dynamical Systems from Sensory Input. Oumayma Bounou, Jean Ponce, Justin Carpentier |
| 2021 | Online Learning in Periodic Zero-Sum Games. Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian J. Ratliff |
| 2021 | Online Market Equilibrium with Application to Fair Division. Yuan Gao, Alex Peysakhovich, Christian Kroer |
| 2021 | Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm. Nathan Noiry, Vianney Perchet, Flore Sentenac |
| 2021 | Online Multi-Armed Bandits with Adaptive Inference. Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou |
| 2021 | Online Robust Reinforcement Learning with Model Uncertainty. Yue Wang, Shaofeng Zou |
| 2021 | Online Selective Classification with Limited Feedback. Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama |
| 2021 | Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits. Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet |
| 2021 | Online Variational Filtering and Parameter Learning. Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet |
| 2021 | Online and Offline Reinforcement Learning by Planning with a Learned Model. Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver |
| 2021 | Online false discovery rate control for anomaly detection in time series. Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot |
| 2021 | Online learning in MDPs with linear function approximation and bandit feedback. Gergely Neu, Julia Olkhovskaya |
| 2021 | Only Train Once: A One-Shot Neural Network Training And Pruning Framework. Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang, Zhihui Zhu, Luming Liang, Yixin Shi, Sheng Yi, Xiao Tu |
| 2021 | Open Rule Induction. Wanyun Cui, Xingran Chen |
| 2021 | Open-set Label Noise Can Improve Robustness Against Inherent Label Noise. Hongxin Wei, Lue Tao, Renchunzi Xie, Bo An |
| 2021 | OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization. Kuniaki Saito, Donghyun Kim, Kate Saenko |
| 2021 | Optimal Algorithms for Stochastic Contextual Preference Bandits. Aadirupa Saha |
| 2021 | Optimal Best-Arm Identification Methods for Tail-Risk Measures. Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja |
| 2021 | Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang |
| 2021 | Optimal Order Simple Regret for Gaussian Process Bandits. Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu |
| 2021 | Optimal Policies Tend To Seek Power. Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli |
| 2021 | Optimal Rates for Nonparametric Density Estimation under Communication Constraints. Jayadev Acharya, Clément L. Canonne, Aditya Vikram Singh, Himanshu Tyagi |
| 2021 | Optimal Rates for Random Order Online Optimization. Uri Sherman, Tomer Koren, Yishay Mansour |
| 2021 | Optimal Sketching for Trace Estimation. Shuli Jiang, Hai Pham, David P. Woodruff, Qiuyi (Richard) Zhang |
| 2021 | Optimal Underdamped Langevin MCMC Method. Zhengmian Hu, Feihu Huang, Heng Huang |
| 2021 | Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. Ming Yin, Yu-Xiang Wang |
| 2021 | Optimal prediction of Markov chains with and without spectral gap. Yanjun Han, Soham Jana, Yihong Wu |
| 2021 | Optimality and Stability in Federated Learning: A Game-theoretic Approach. Kate Donahue, Jon M. Kleinberg |
| 2021 | Optimality of variational inference for stochasticblock model with missing links. Solenne Gaucher, Olga Klopp |
| 2021 | Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning. Ali Taghibakhshi, Scott P. MacLachlan, Luke N. Olson, Matthew West |
| 2021 | Optimizing Conditional Value-At-Risk of Black-Box Functions. Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD. Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu |
| 2021 | Optimizing Reusable Knowledge for Continual Learning via Metalearning. Julio Hurtado, Alain Raymond-Saez, Alvaro Soto |
| 2021 | Oracle Complexity in Nonsmooth Nonconvex Optimization. Guy Kornowski, Ohad Shamir |
| 2021 | Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure. Aviv Rosenberg, Yishay Mansour |
| 2021 | Out-of-Distribution Generalization in Kernel Regression. Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan |
| 2021 | Outcome-Driven Reinforcement Learning via Variational Inference. Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine |
| 2021 | Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima. Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, Xiao-Ming Wu |
| 2021 | Overcoming the Convex Barrier for Simplex Inputs. Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham |
| 2021 | Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. Vivien Cabannes, Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi |
| 2021 | Overinterpretation reveals image classification model pathologies. Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford |
| 2021 | Overlapping Spaces for Compact Graph Representations. Kirill Shevkunov, Liudmila Prokhorenkova |
| 2021 | Overparameterization Improves Robustness to Covariate Shift in High Dimensions. Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington |
| 2021 | PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition. Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu, Shiyu Chang, Yi-Lun Liao, Yung-Sung Chuang, Kaizhi Qian, Sameer Khurana, David D. Cox, James R. Glass |
| 2021 | PCA Initialization for Approximate Message Passing in Rotationally Invariant Models. Marco Mondelli, Ramji Venkataramanan |
| 2021 | PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. Moshe Eliasof, Eldad Haber, Eran Treister |
| 2021 | PLUGIn: A simple algorithm for inverting generative models with recovery guarantees. Babhru Joshi, Xiaowei Li, Yaniv Plan, Özgür Yilmaz |
| 2021 | PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair. Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra |
| 2021 | POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples. Duong H. Le, Khoi D. Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen, Binh-Son Hua |
| 2021 | PSD Representations for Effective Probability Models. Alessandro Rudi, Carlo Ciliberto |
| 2021 | PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning. Yining Hong, Li Yi, Josh Tenenbaum, Antonio Torralba, Chuang Gan |
| 2021 | Panoptic 3D Scene Reconstruction From a Single RGB Image. Manuel Dahnert, Ji Hou, Matthias Nießner, Angela Dai |
| 2021 | ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco |
| 2021 | Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. Samuel Daulton, Maximilian Balandat, Eytan Bakshy |
| 2021 | Parallel and Efficient Hierarchical k-Median Clustering. Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson |
| 2021 | Parallelizing Thompson Sampling. Amin Karbasi, Vahab S. Mirrokni, Mohammad Shadravan |
| 2021 | Parameter Inference with Bifurcation Diagrams. Gregory Szép, Neil Dalchau, Attila Csikász-Nagy |
| 2021 | Parameter Prediction for Unseen Deep Architectures. Boris Knyazev, Michal Drozdzal, Graham W. Taylor, Adriana Romero-Soriano |
| 2021 | Parameter-free HE-friendly Logistic Regression. Junyoung Byun, Woojin Lee, Jaewook Lee |
| 2021 | Parameterized Knowledge Transfer for Personalized Federated Learning. Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, Feijie Wu |
| 2021 | Parametric Complexity Bounds for Approximating PDEs with Neural Networks. Tanya Marwah, Zachary C. Lipton, Andrej Risteski |
| 2021 | Parametrized Quantum Policies for Reinforcement Learning. Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko |
| 2021 | Pareto Domain Adaptation. Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang |
| 2021 | Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems. Bo Sun, Russell Lee, Mohammad H. Hajiesmaili, Adam Wierman, Danny H. K. Tsang |
| 2021 | Partial success in closing the gap between human and machine vision. Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel |
| 2021 | PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization. Benyuan Sun, Hongxing Huo, Yi Yang, Bo Bai |
| 2021 | Particle Cloud Generation with Message Passing Generative Adversarial Networks. Raghav Kansal, Javier M. Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos |
| 2021 | Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. Atsushi Nitanda, Denny Wu, Taiji Suzuki |
| 2021 | Partition and Code: learning how to compress graphs. Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein |
| 2021 | Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks. Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener |
| 2021 | Passive attention in artificial neural networks predicts human visual selectivity. Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby |
| 2021 | PatchGame: Learning to Signal Mid-level Patches in Referential Games. Kamal Gupta, Gowthami Somepalli, Anubhav Gupta, Vinoj Yasanga Jayasundara Magalle Hewa, Matthias Zwicker, Abhinav Shrivastava |
| 2021 | Pay Attention to MLPs. Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le |
| 2021 | Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling. Hongyu Gong, Yun Tang, Juan Miguel Pino, Xian Li |
| 2021 | Per-Pixel Classification is Not All You Need for Semantic Segmentation. Bowen Cheng, Alexander G. Schwing, Alexander Kirillov |
| 2021 | PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators. Anish Agarwal, Abdullah Omar Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang |
| 2021 | Perceptual Score: What Data Modalities Does Your Model Perceive? Itai Gat, Idan Schwartz, Alexander G. Schwing |
| 2021 | Periodic Activation Functions Induce Stationarity. Lassi Meronen, Martin Trapp, Arno Solin |
| 2021 | Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. Robin Winter, Frank Noé, Djork-Arné Clevert |
| 2021 | Permuton-induced Chinese Restaurant Process. Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda |
| 2021 | Personalized Federated Learning With Gaussian Processes. Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya |
| 2021 | Perturb-and-max-product: Sampling and learning in discrete energy-based models. Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George |
| 2021 | Perturbation Theory for the Information Bottleneck. Vudtiwat Ngampruetikorn, David J. Schwab |
| 2021 | Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems. Yiheng Lin, Yang Hu, Guanya Shi, Haoyuan Sun, Guannan Qu, Adam Wierman |
| 2021 | Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao |
| 2021 | PettingZoo: Gym for Multi-Agent Reinforcement Learning. J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S. Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, Niall L. Williams, Yashas Lokesh, Praveen Ravi |
| 2021 | Photonic Differential Privacy with Direct Feedback Alignment. Ruben Ohana, Hamlet Jesse Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy |
| 2021 | Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry |
| 2021 | Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling. Naoya Takeishi, Alexandros Kalousis |
| 2021 | PiRank: Scalable Learning To Rank via Differentiable Sorting. Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon |
| 2021 | Pipeline Combinators for Gradual AutoML. Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay |
| 2021 | Piper: Multidimensional Planner for DNN Parallelization. Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee |
| 2021 | Planning from Pixels in Environments with Combinatorially Hard Search Spaces. Marco Bagatella, Miroslav Olsák, Michal Rolínek, Georg Martius |
| 2021 | Play to Grade: Testing Coding Games as Classifying Markov Decision Process. Allen Nie, Emma Brunskill, Chris Piech |
| 2021 | PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning. Tao Yu, Cuiling Lan, Wenjun Zeng, Mingxiao Feng, Zhizheng Zhang, Zhibo Chen |
| 2021 | Pointwise Bounds for Distribution Estimation under Communication Constraints. Wei-Ning Chen, Peter Kairouz, Ayfer Özgür |
| 2021 | PolarStream: Streaming Object Detection and Segmentation with Polar Pillars. Qi Chen, Sourabh Vora, Oscar Beijbom |
| 2021 | Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai |
| 2021 | Policy Learning Using Weak Supervision. Jingkang Wang, Hongyi Guo, Zhaowei Zhu, Yang Liu |
| 2021 | Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses. Haipeng Luo, Chen-Yu Wei, Chung-wei Lee |
| 2021 | Pooling by Sliced-Wasserstein Embedding. Navid Naderializadeh, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann, Soheil Kolouri |
| 2021 | PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Yi Ren, Jinglin Liu, Zhou Zhao |
| 2021 | Post-Contextual-Bandit Inference. Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan |
| 2021 | Post-Training Quantization for Vision Transformer. Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, Wen Gao |
| 2021 | Post-Training Sparsity-Aware Quantization. Gil Shomron, Freddy Gabbay, Samer Kurzum, Uri C. Weiser |
| 2021 | Post-processing for Individual Fairness. Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin |
| 2021 | Posterior Collapse and Latent Variable Non-identifiability. Yixin Wang, David M. Blei, John P. Cunningham |
| 2021 | Posterior Meta-Replay for Continual Learning. Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento |
| 2021 | Powerpropagation: A sparsity inducing weight reparameterisation. Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh |
| 2021 | Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient. David L. Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy |
| 2021 | Practical Near Neighbor Search via Group Testing. Joshua Engels, Benjamin Coleman, Anshumali Shrivastava |
| 2021 | Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. Julian Katz-Samuels, Blake Mason, Kevin Jamieson, Robert Nowak |
| 2021 | Pragmatic Image Compression for Human-in-the-Loop Decision-Making. Siddharth Reddy, Anca D. Dragan, Sergey Levine |
| 2021 | Precise characterization of the prior predictive distribution of deep ReLU networks. Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann |
| 2021 | Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization. Jialun Zhang, Salar Fattahi, Richard Y. Zhang |
| 2021 | Predicting Deep Neural Network Generalization with Perturbation Response Curves. Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen |
| 2021 | Predicting Event Memorability from Contextual Visual Semantics. Qianli Xu, Fen Fang, Ana Garcia del Molino, Vigneshwaran Subbaraju, Joo-Hwee Lim |
| 2021 | Predicting Molecular Conformation via Dynamic Graph Score Matching. Shitong Luo, Chence Shi, Minkai Xu, Jian Tang |
| 2021 | Predicting What You Already Know Helps: Provable Self-Supervised Learning. Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo |
| 2021 | Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics. Bhavin Choksi, Milad Mozafari, Callum Biggs O'May, Benjamin Ador, Andrea Alamia, Rufin VanRullen |
| 2021 | PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning. Neehar Peri, Michael J. Curry, Samuel Dooley, John Dickerson |
| 2021 | Preserved central model for faster bidirectional compression in distributed settings. Constantin Philippenko, Aymeric Dieuleveut |
| 2021 | Pretraining Representations for Data-Efficient Reinforcement Learning. Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville |
| 2021 | Prior-independent Dynamic Auctions for a Value-maximizing Buyer. Yuan Deng, Hanrui Zhang |
| 2021 | Private Non-smooth ERM and SCO in Subquadratic Steps. Janardhan Kulkarni, Yin Tat Lee, Daogao Liu |
| 2021 | Private and Non-private Uniformity Testing for Ranking Data. Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis |
| 2021 | Private learning implies quantum stability. Yihui Quek, Srinivasan Arunachalam, John A. Smolin |
| 2021 | Privately Learning Mixtures of Axis-Aligned Gaussians. Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw |
| 2021 | Privately Learning Subspaces. Vikrant Singhal, Thomas Steinke |
| 2021 | Privately Publishable Per-instance Privacy. Rachel Redberg, Yu-Xiang Wang |
| 2021 | ProTo: Program-Guided Transformer for Program-Guided Tasks. Zelin Zhao, Karan Samel, Binghong Chen, Le Song |
| 2021 | Probabilistic Attention for Interactive Segmentation. Prasad Gabbur, Manjot Bilkhu, Javier R. Movellan |
| 2021 | Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs. Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy |
| 2021 | Probabilistic Forecasting: A Level-Set Approach. Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus |
| 2021 | Probabilistic Margins for Instance Reweighting in Adversarial Training. Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama |
| 2021 | Probabilistic Tensor Decomposition of Neural Population Spiking Activity. Hugo Soulat, Sepiedeh Keshavarzi, Troy W. Margrie, Maneesh Sahani |
| 2021 | Probabilistic Transformer For Time Series Analysis. Binh Tang, David S. Matteson |
| 2021 | Probability Paths and the Structure of Predictions over Time. Zhiyuan (Jerry) Lin, Hao Sheng, Sharad Goel |
| 2021 | Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training. Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo |
| 2021 | Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets. Irene Solaiman, Christy Dennison |
| 2021 | Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent. Xingchao Liu, Xin Tong, Qiang Liu |
| 2021 | Program Synthesis Guided Reinforcement Learning for Partially Observed Environments. Yichen Yang, Jeevana Priya Inala, Osbert Bastani, Yewen Pu, Armando Solar-Lezama, Martin C. Rinard |
| 2021 | Progressive Coordinate Transforms for Monocular 3D Object Detection. Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue |
| 2021 | Progressive Feature Interaction Search for Deep Sparse Network. Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li |
| 2021 | Projected GANs Converge Faster. Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger |
| 2021 | Proper Value Equivalence. Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh |
| 2021 | Property-Aware Relation Networks for Few-Shot Molecular Property Prediction. Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, Dejing Dou |
| 2021 | Proportional Participatory Budgeting with Additive Utilities. Dominik Peters, Grzegorz Pierczynski, Piotr Skowron |
| 2021 | Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation. Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu |
| 2021 | Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. Andrea Zanette, Martin J. Wainwright, Emma Brunskill |
| 2021 | Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma |
| 2021 | Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. Kefan Dong, Jiaqi Yang, Tengyu Ma |
| 2021 | Provable Representation Learning for Imitation with Contrastive Fourier Features. Ofir Nachum, Mengjiao Yang |
| 2021 | Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning. Guanlin Liu, Lifeng Lai |
| 2021 | Provably Efficient Causal Reinforcement Learning with Confounded Observational Data. Lingxiao Wang, Zhuoran Yang, Zhaoran Wang |
| 2021 | Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. Tianhao Wang, Dongruo Zhou, Quanquan Gu |
| 2021 | Provably Faster Algorithms for Bilevel Optimization. Junjie Yang, Kaiyi Ji, Yingbin Liang |
| 2021 | Provably Strict Generalisation Benefit for Invariance in Kernel Methods. Bryn Elesedy |
| 2021 | Provably efficient multi-task reinforcement learning with model transfer. Chicheng Zhang, Zhi Wang |
| 2021 | Provably efficient, succinct, and precise explanations. Guy Blanc, Jane Lange, Li-Yang Tan |
| 2021 | Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. Spencer Frei, Quanquan Gu |
| 2021 | Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence. Antoine Labatie, Dominic Masters, Zach Eaton-Rosen, Carlo Luschi |
| 2021 | Pruning Randomly Initialized Neural Networks with Iterative Randomization. Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue |
| 2021 | Pseudo-Spherical Contrastive Divergence. Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon |
| 2021 | Pure Exploration in Kernel and Neural Bandits. Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak |
| 2021 | Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples. Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, Jinho Lee |
| 2021 | Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Tudor Dumitras |
| 2021 | QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi |
| 2021 | Quantifying and Improving Transferability in Domain Generalization. Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart |
| 2021 | R-Drop: Regularized Dropout for Neural Networks. Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu |
| 2021 | RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks. Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly |
| 2021 | REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision. Mihai Fieraru, Mihai Zanfir, Teodor Alexandru Szente, Eduard Gabriel Bazavan, Vlad Olaru, Cristian Sminchisescu |
| 2021 | RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning. Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh K. Iyer |
| 2021 | RIM: Reliable Influence-based Active Learning on Graphs. Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui |
| 2021 | RL for Latent MDPs: Regret Guarantees and a Lower Bound. Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2021 | RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem. Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica |
| 2021 | RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents. Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich |
| 2021 | RMM: Reinforced Memory Management for Class-Incremental Learning. Yaoyao Liu, Bernt Schiele, Qianru Sun |
| 2021 | Random Noise Defense Against Query-Based Black-Box Attacks. Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu |
| 2021 | Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. Itay Safran, Ohad Shamir |
| 2021 | Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery. Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu |
| 2021 | Ranking Policy Decisions. Hadrien Pouget, Hana Chockler, Youcheng Sun, Daniel Kroening |
| 2021 | Rate-Optimal Subspace Estimation on Random Graphs. Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang |
| 2021 | Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections. Nabarun Deb, Promit Ghosal, Bodhisattva Sen |
| 2021 | Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler. Aditya Desai, Zhaozhuo Xu, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava |
| 2021 | Re-ranking for image retrieval and transductive few-shot classification. Xi Shen, Yang Xiao, Shell Xu Hu, Othman Sbai, Mathieu Aubry |
| 2021 | ReAct: Out-of-distribution Detection With Rectified Activations. Yiyou Sun, Chuan Guo, Yixuan Li |
| 2021 | ReLU Regression with Massart Noise. Ilias Diakonikolas, Jongho Park, Christos Tzamos |
| 2021 | ReSSL: Relational Self-Supervised Learning with Weak Augmentation. Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, Chang Xu |
| 2021 | Realistic evaluation of transductive few-shot learning. Olivier Veilleux, Malik Boudiaf, Pablo Piantanida, Ismail Ben Ayed |
| 2021 | Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training. Minguk Kang, Woohyeon Shim, Minsu Cho, Jaesik Park |
| 2021 | Rebounding Bandits for Modeling Satiation Effects. Liu Leqi, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery |
| 2021 | Recognizing Vector Graphics without Rasterization. Xinyang Jiang, Lu Liu, Caihua Shan, Yifei Shen, Xuanyi Dong, Dongsheng Li |
| 2021 | Reconstruction for Powerful Graph Representations. Leonardo Cotta, Christopher Morris, Bruno Ribeiro |
| 2021 | Recovering Latent Causal Factor for Generalization to Distributional Shifts. Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu |
| 2021 | Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition. Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek Kamilov |
| 2021 | Rectangular Flows for Manifold Learning. Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham |
| 2021 | Rectifying the Shortcut Learning of Background for Few-Shot Learning. Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie, Zenglin Xu, Qi Tian |
| 2021 | Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation. Jingyu Zhao, Yanwen Fang, Guodong Li |
| 2021 | Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification. Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner |
| 2021 | Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits. Orestis Papadigenopoulos, Constantine Caramanis |
| 2021 | Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks. Robert Lieck, Martin Rohrmeier |
| 2021 | Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias. Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash |
| 2021 | Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems. Subhabrata Dutta, Tanya Gautam, Soumen Chakrabarti, Tanmoy Chakraborty |
| 2021 | Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks. Chenning Yu, Sicun Gao |
| 2021 | Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation. Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon |
| 2021 | Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment. Yin Zhao, Minquan Wang, Longjun Cai |
| 2021 | Referring Transformer: A One-step Approach to Multi-task Visual Grounding. Muchen Li, Leonid Sigal |
| 2021 | Refined Learning Bounds for Kernel and Approximate $k$-Means. Yong Liu |
| 2021 | Refining Language Models with Compositional Explanations. Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, Xiang Ren |
| 2021 | Reformulating Zero-shot Action Recognition for Multi-label Actions. Alec Kerrigan, Kevin Duarte, Yogesh S. Rawat, Mubarak Shah |
| 2021 | Regime Switching Bandits. Xiang Zhou, Yi Xiong, Ningyuan Chen, Xuefeng Gao |
| 2021 | Regret Bounds for Gaussian-Process Optimization in Large Domains. Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause |
| 2021 | Regret Minimization Experience Replay in Off-Policy Reinforcement Learning. Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang, Shengyi Jiang, Feng Xu, Yang Yu |
| 2021 | Regularization in ResNet with Stochastic Depth. Soufiane Hayou, Fadhel Ayed |
| 2021 | Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond. D. Khuê Lê-Huu, Karteek Alahari |
| 2021 | Regularized Softmax Deep Multi-Agent Q-Learning. Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson |
| 2021 | Regulating algorithmic filtering on social media. Sarah Huiyi Cen, Devavrat Shah |
| 2021 | Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization. Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu |
| 2021 | Reinforcement Learning Enhanced Explainer for Graph Neural Networks. Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li |
| 2021 | Reinforcement Learning based Disease Progression Model for Alzheimer's Disease. Krishnakant V. Saboo, Anirudh Choudhary, Yurui Cao, Gregory A. Worrell, David T. Jones, Ravishankar K. Iyer |
| 2021 | Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta |
| 2021 | Reinforcement Learning in Newcomblike Environments. James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse |
| 2021 | Reinforcement Learning in Reward-Mixing MDPs. Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2021 | Reinforcement Learning with Latent Flow. Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin |
| 2021 | Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes. Hyunji Alex Nam, Scott L. Fleming, Emma Brunskill |
| 2021 | Reinforcement learning for optimization of variational quantum circuit architectures. Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko |
| 2021 | Relational Self-Attention: What's Missing in Attention for Video Understanding. Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, Minsu Cho |
| 2021 | Relative Flatness and Generalization. Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, Mario Boley |
| 2021 | Relative Uncertainty Learning for Facial Expression Recognition. Yuhang Zhang, Chengrui Wang, Weihong Deng |
| 2021 | Relative stability toward diffeomorphisms indicates performance in deep nets. Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart |
| 2021 | Relaxed Marginal Consistency for Differentially Private Query Answering. Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau |
| 2021 | Relaxing Local Robustness. Klas Leino, Matt Fredrikson |
| 2021 | RelaySum for Decentralized Deep Learning on Heterogeneous Data. Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi |
| 2021 | Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang |
| 2021 | Reliable Decisions with Threshold Calibration. Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon |
| 2021 | Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space. Sandesh Ghimire, Aria Masoomi, Jennifer G. Dy |
| 2021 | Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju |
| 2021 | Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection. Chunjong Park, Anas Awadalla, Tadayoshi Kohno, Shwetak N. Patel |
| 2021 | Remember What You Want to Forget: Algorithms for Machine Unlearning. Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh |
| 2021 | Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience. Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert, Benjamin A. Dunn, Philipp Berens, David A. Klindt, Thomas Euler |
| 2021 | Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2021 | Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification. Ben Eysenbach, Sergey Levine, Ruslan Salakhutdinov |
| 2021 | Replay-Guided Adversarial Environment Design. Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel |
| 2021 | Representation Costs of Linear Neural Networks: Analysis and Design. Zhen Dai, Mina Karzand, Nathan Srebro |
| 2021 | Representation Learning Beyond Linear Prediction Functions. Ziping Xu, Ambuj Tewari |
| 2021 | Representation Learning for Event-based Visuomotor Policies. Sai Vemprala, Sami Mian, Ashish Kapoor |
| 2021 | Representation Learning on Spatial Networks. Zheng Zhan, Liang Zhao |
| 2021 | Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models. Yi Sui, Ga Wu, Scott Sanner |
| 2021 | Representing Hyperbolic Space Accurately using Multi-Component Floats. Tao Yu, Christopher De Sa |
| 2021 | Representing Long-Range Context for Graph Neural Networks with Global Attention. Zhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, Ion Stoica |
| 2021 | Repulsive Deep Ensembles are Bayesian. Francesco D'Angelo, Vincent Fortuin |
| 2021 | ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees. Kuan-Lin Chen, Ching Hua Lee, Harinath Garudadri, Bhaskar D. Rao |
| 2021 | ResT: An Efficient Transformer for Visual Recognition. Qinglong Zhang, Yu-Bin Yang |
| 2021 | Residual Pathway Priors for Soft Equivariance Constraints. Marc Finzi, Greg Benton, Andrew Gordon Wilson |
| 2021 | Residual Relaxation for Multi-view Representation Learning. Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2021 | Residual2Vec: Debiasing graph embedding with random graphs. Sadamori Kojaku, Jisung Yoon, Isabel Constantino, Yong-Yeol Ahn |
| 2021 | Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence. Deng-Bao Wang, Lei Feng, Min-Ling Zhang |
| 2021 | Rethinking Graph Transformers with Spectral Attention. Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou |
| 2021 | Rethinking Neural Operations for Diverse Tasks. Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar |
| 2021 | Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation. Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang |
| 2021 | Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization. Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu |
| 2021 | Rethinking conditional GAN training: An approach using geometrically structured latent manifolds. Sameera Ramasinghe, Moshiur R. Farazi, Salman H. Khan, Nick Barnes, Stephen Gould |
| 2021 | Rethinking gradient sparsification as total error minimization. Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini, Panos Kalnis |
| 2021 | Rethinking the Pruning Criteria for Convolutional Neural Network. Zhongzhan Huang, Wenqi Shao, Xinjiang Wang, Liang Lin, Ping Luo |
| 2021 | Rethinking the Variational Interpretation of Accelerated Optimization Methods. Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand |
| 2021 | Retiring Adult: New Datasets for Fair Machine Learning. Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt |
| 2021 | Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes. Jai Moondra, Hassan Mortagy, Swati Gupta |
| 2021 | Revealing and Protecting Labels in Distributed Training. Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays |
| 2021 | Revenue maximization via machine learning with noisy data. Ellen Vitercik, Tom Yan |
| 2021 | Reverse engineering learned optimizers reveals known and novel mechanisms. Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein |
| 2021 | Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. Jimmy T. H. Smith, Scott W. Linderman, David Sussillo |
| 2021 | Reverse-Complement Equivariant Networks for DNA Sequences. Vincent Mallet, Jean-Philippe Vert |
| 2021 | Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning. Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man Cheung |
| 2021 | Revisiting 3D Object Detection From an Egocentric Perspective. Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas A. Funkhouser, Yin Zhou, Dragomir Anguelov |
| 2021 | Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Luc Van Gool |
| 2021 | Revisiting Deep Learning Models for Tabular Data. Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko |
| 2021 | Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme. Shaojie Li, Jie Wu, Xuefeng Xiao, Fei Chao, Xudong Mao, Rongrong Ji |
| 2021 | Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness. Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer G. Dy |
| 2021 | Revisiting Model Stitching to Compare Neural Representations. Yamini Bansal, Preetum Nakkiran, Boaz Barak |
| 2021 | Revisiting ResNets: Improved Training and Scaling Strategies. Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph |
| 2021 | Revisiting Smoothed Online Learning. Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang |
| 2021 | Revisiting the Calibration of Modern Neural Networks. Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic |
| 2021 | Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning. Chongjian Ge, Youwei Liang, Yibing Song, Jianbo Jiao, Jue Wang, Ping Luo |
| 2021 | Reward is enough for convex MDPs. Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh |
| 2021 | Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. Weitong Zhang, Dongruo Zhou, Quanquan Gu |
| 2021 | Risk Bounds and Calibration for a Smart Predict-then-Optimize Method. Heyuan Liu, Paul Grigas |
| 2021 | Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. Yuan Cao, Quanquan Gu, Mikhail Belkin |
| 2021 | Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan |
| 2021 | Risk Monotonicity in Statistical Learning. Zakaria Mhammedi |
| 2021 | Risk-Averse Bayes-Adaptive Reinforcement Learning. Marc Rigter, Bruno Lacerda, Nick Hawes |
| 2021 | Risk-Aware Transfer in Reinforcement Learning using Successor Features. Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee |
| 2021 | Risk-averse Heteroscedastic Bayesian Optimization. Anastasia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause |
| 2021 | RoMA: Robust Model Adaptation for Offline Model-based Optimization. Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin |
| 2021 | Robust Allocations with Diversity Constraints. Zeyu Shen, Lodewijk Gelauff, Ashish Goel, Aleksandra Korolova, Kamesh Munagala |
| 2021 | Robust Auction Design in the Auto-bidding World. Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo |
| 2021 | Robust Compressed Sensing MRI with Deep Generative Priors. Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G. Dimakis, Jonathan I. Tamir |
| 2021 | Robust Contrastive Learning Using Negative Samples with Diminished Semantics. Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, David Jacobs |
| 2021 | Robust Counterfactual Explanations on Graph Neural Networks. Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang |
| 2021 | Robust Deep Reinforcement Learning through Adversarial Loss. Tuomas P. Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng |
| 2021 | Robust Generalization despite Distribution Shift via Minimum Discriminating Information. Tobias Sutter, Andreas Krause, Daniel Kuhn |
| 2021 | Robust Implicit Networks via Non-Euclidean Contractions. Saber Jafarpour, Alexander Davydov, Anton V. Proskurnikov, Francesco Bullo |
| 2021 | Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch. Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Adrian Weller, Volkan Cevher |
| 2021 | Robust Learning of Optimal Auctions. Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis |
| 2021 | Robust Online Correlation Clustering. Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou |
| 2021 | Robust Optimization for Multilingual Translation with Imbalanced Data. Xian Li, Hongyu Gong |
| 2021 | Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference. Dongkai Wang, Shiliang Zhang, Gang Hua |
| 2021 | Robust Predictable Control. Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine |
| 2021 | Robust Regression Revisited: Acceleration and Improved Estimation Rates. Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian |
| 2021 | Robust Visual Reasoning via Language Guided Neural Module Networks. Arjun R. Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu |
| 2021 | Robust and Decomposable Average Precision for Image Retrieval. Elias Ramzi, Nicolas Thome, Clément Rambour, Nicolas Audebert, Xavier Bitot |
| 2021 | Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems. Zixiu Wang, Yiwen Guo, Hu Ding |
| 2021 | Robust and differentially private mean estimation. Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh |
| 2021 | Robustifying Algorithms of Learning Latent Trees with Vector Variables. Fengzhuo Zhang, Vincent Y. F. Tan |
| 2021 | Robustness between the worst and average case. Leslie Rice, Anna Bair, Huan Zhang, J. Zico Kolter |
| 2021 | Robustness of Graph Neural Networks at Scale. Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann |
| 2021 | Robustness via Uncertainty-aware Cycle Consistency. Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata |
| 2021 | Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding. Tengwei Song, Jie Luo, Lei Huang |
| 2021 | Roto-translated Local Coordinate Frames For Interacting Dynamical Systems. Miltiadis Kofinas, Naveen Shankar Nagaraja, Efstratios Gavves |
| 2021 | Row-clustering of a Point Process-valued Matrix. Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan |
| 2021 | S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia |
| 2021 | SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL. Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao |
| 2021 | SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization. Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or |
| 2021 | SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization. Ziming Zhang, Yun Yue, Guojun Wu, Yanhua Li, Haichong K. Zhang |
| 2021 | SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller |
| 2021 | SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency. Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslan Salakhutdinov |
| 2021 | SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. Ayush Sekhari, Karthik Sridharan, Satyen Kale |
| 2021 | SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark. Victor Zhong, Austin W. Hanjie, Sida I. Wang, Karthik Narasimhan, Luke Zettlemoyer |
| 2021 | SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios. Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, Rishabh K. Iyer |
| 2021 | SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess |
| 2021 | SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks. Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi |
| 2021 | SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour |
| 2021 | SNIPS: Solving Noisy Inverse Problems Stochastically. Bahjat Kawar, Gregory Vaksman, Michael Elad |
| 2021 | SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation. Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra |
| 2021 | SOFT: Softmax-free Transformer with Linear Complexity. Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing Xu, Tao Xiang, Li Zhang |
| 2021 | SOLQ: Segmenting Objects by Learning Queries. Bin Dong, Fangao Zeng, Tiancai Wang, Xiangyu Zhang, Yichen Wei |
| 2021 | SOPE: Spectrum of Off-Policy Estimators. Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum |
| 2021 | SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang |
| 2021 | SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning. Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas |
| 2021 | SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection. Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali |
| 2021 | SSMF: Shifting Seasonal Matrix Factorization. Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi |
| 2021 | SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon |
| 2021 | STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod K. Varshney |
| 2021 | STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data. Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yufeng Li, Shiliang Pu |
| 2021 | STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization. Kfir Y. Levy, Ali Kavis, Volkan Cevher |
| 2021 | SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients. Feihu Huang, Junyi Li, Heng Huang |
| 2021 | SWAD: Domain Generalization by Seeking Flat Minima. Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, Sungrae Park |
| 2021 | Safe Policy Optimization with Local Generalized Linear Function Approximations. Akifumi Wachi, Yunyue Wei, Yanan Sui |
| 2021 | Safe Pontryagin Differentiable Programming. Wanxin Jin, Shaoshuai Mou, George J. Pappas |
| 2021 | Safe Reinforcement Learning by Imagining the Near Future. Garrett Thomas, Yuping Luo, Tengyu Ma |
| 2021 | Safe Reinforcement Learning with Natural Language Constraints. Tsung-Yen Yang, Michael Y. Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan |
| 2021 | Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon |
| 2021 | SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning. Aaron Chan, Jiashu Xu, Boyuan Long, Soumya Sanyal, Tanishq Gupta, Xiang Ren |
| 2021 | Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons. Wenbo Ren, Jia Liu, Ness B. Shroff |
| 2021 | Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik |
| 2021 | Sample Selection for Fair and Robust Training. Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh |
| 2021 | Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. Yu Bai, Chi Jin, Huan Wang, Caiming Xiong |
| 2021 | Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting. Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei |
| 2021 | Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model. Bingyan Wang, Yuling Yan, Jianqing Fan |
| 2021 | Sampling with Trusthworthy Constraints: A Variational Gradient Framework. Xingchao Liu, Xin Tong, Qiang Liu |
| 2021 | Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang |
| 2021 | Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. Kristopher T. Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin |
| 2021 | Scalable Diverse Model Selection for Accessible Transfer Learning. Daniel Bolya, Rohit Mittapalli, Judy Hoffman |
| 2021 | Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation. Arno Solin, Ella Tamir, Prakhar Verma |
| 2021 | Scalable Inference of Sparsely-changing Gaussian Markov Random Fields. Salar Fattahi, Andrés Gómez |
| 2021 | Scalable Intervention Target Estimation in Linear Models. Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer |
| 2021 | Scalable Neural Data Server: A Data Recommender for Transfer Learning. Tianshi Cao, Sasha Doubov, David Acuna, Sanja Fidler |
| 2021 | Scalable Online Planning via Reinforcement Learning Fine-Tuning. Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown |
| 2021 | Scalable Quasi-Bayesian Inference for Instrumental Variable Regression. Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu |
| 2021 | Scalable Rule-Based Representation Learning for Interpretable Classification. Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang |
| 2021 | Scalable Thompson Sampling using Sparse Gaussian Process Models. Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny |
| 2021 | Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints. Harry Bendekgey, Erik B. Sudderth |
| 2021 | Scalars are universal: Equivariant machine learning, structured like classical physics. Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith |
| 2021 | ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers. Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling Liang, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen |
| 2021 | Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets. Max Ryabinin, Andrey Malinin, Mark J. F. Gales |
| 2021 | Scaling Gaussian Processes with Derivative Information Using Variational Inference. Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel |
| 2021 | Scaling Neural Tangent Kernels via Sketching and Random Features. Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin |
| 2021 | Scaling Up Exact Neural Network Compression by ReLU Stability. Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam |
| 2021 | Scaling Vision with Sparse Mixture of Experts. Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby |
| 2021 | Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification. Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka |
| 2021 | Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning. Jiani Huang, Ziyang Li, Binghong Chen, Karan Samel, Mayur Naik, Le Song, Xujie Si |
| 2021 | Scatterbrain: Unifying Sparse and Low-rank Attention. Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré |
| 2021 | Scheduling jobs with stochastic holding costs. Dabeen Lee, Milan Vojnovic |
| 2021 | Score-based Generative Modeling in Latent Space. Arash Vahdat, Karsten Kreis, Jan Kautz |
| 2021 | Score-based Generative Neural Networks for Large-Scale Optimal Transport. Grady Daniels, Tyler Maunu, Paul Hand |
| 2021 | Searching Parameterized AP Loss for Object Detection. Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai |
| 2021 | Searching for Efficient Transformers for Language Modeling. David R. So, Wojciech Manke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le |
| 2021 | Searching the Search Space of Vision Transformer. Minghao Chen, Kan Wu, Bolin Ni, Houwen Peng, Bei Liu, Jianlong Fu, Hongyang Chao, Haibin Ling |
| 2021 | Second-Order Neural ODE Optimizer. Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou |
| 2021 | See More for Scene: Pairwise Consistency Learning for Scene Classification. Gongwei Chen, Xinhang Song, Bohan Wang, Shuqiang Jiang |
| 2021 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, José M. Álvarez, Ping Luo |
| 2021 | Selective Sampling for Online Best-arm Identification. Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson |
| 2021 | Self-Adaptable Point Processes with Nonparametric Time Decays. Zhimeng Pan, Zheng Wang, Jeff M. Phillips, Shandian Zhe |
| 2021 | Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal |
| 2021 | Self-Consistent Models and Values. Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver |
| 2021 | Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks. Jinhee Lee, Haeri Kim, Youngkyu Hong, Hye Won Chung |
| 2021 | Self-Instantiated Recurrent Units with Dynamic Soft Recursion. Aston Zhang, Yi Tay, Yikang Shen, Alvin Chan, Shuai Zhang |
| 2021 | Self-Interpretable Model with Transformation Equivariant Interpretation. Yipei Wang, Xiaoqian Wang |
| 2021 | Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels. Jizong Peng, Ping Wang, Christian Desrosiers, Marco Pedersoli |
| 2021 | Self-Supervised Bug Detection and Repair. Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt |
| 2021 | Self-Supervised GANs with Label Augmentation. Liang Hou, Huawei Shen, Qi Cao, Xueqi Cheng |
| 2021 | Self-Supervised Learning Disentangled Group Representation as Feature. Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang |
| 2021 | Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks. Jesse J. Hagenaars, Federico Paredes-Vallés, Guido de Croon |
| 2021 | Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello |
| 2021 | Self-Supervised Learning with Kernel Dependence Maximization. Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton |
| 2021 | Self-Supervised Multi-Object Tracking with Cross-input Consistency. Favyen Bastani, Songtao He, Samuel Madden |
| 2021 | Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction. Konstantin Schürholt, Dimche Kostadinov, Damian Borth |
| 2021 | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning. Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, Liwei Wang |
| 2021 | Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification. Tong Chen, Jean B. Lasserre, Victor Magron, Edouard Pauwels |
| 2021 | Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics. Carles Domingo-Enrich, Youssef Mroueh |
| 2021 | Sequence-to-Sequence Learning with Latent Neural Grammars. Yoon Kim |
| 2021 | Sequential Algorithms for Testing Closeness of Distributions. Aadil Oufkir, Omar Fawzi, Nicolas Flammarion, Aurélien Garivier |
| 2021 | Sequential Causal Imitation Learning with Unobserved Confounders. Daniel Kumor, Junzhe Zhang, Elias Bareinboim |
| 2021 | Set Prediction in the Latent Space. Konpat Preechakul, Chawan Piansaddhayanon, Burin Naowarat, Tirasan Khandhawit, Sira Sriswasdi, Ekapol Chuangsuwanich |
| 2021 | Settling the Variance of Multi-Agent Policy Gradients. Jakub Grudzien Kuba, Muning Wen, Linghui Meng, Shangding Gu, Haifeng Zhang, David Mguni, Jun Wang, Yaodong Yang |
| 2021 | Shape As Points: A Differentiable Poisson Solver. Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger |
| 2021 | Shape Registration in the Time of Transformers. Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà |
| 2021 | Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects. Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys |
| 2021 | Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders. Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper |
| 2021 | Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices. Aliakbar Panahi, Seyran Saeedi, Tom Arodz |
| 2021 | Shaping embodied agent behavior with activity-context priors from egocentric video. Tushar Nagarajan, Kristen Grauman |
| 2021 | Shapley Residuals: Quantifying the limits of the Shapley value for explanations. Indra Kumar, Carlos Scheidegger, Suresh Venkatasubramanian, Sorelle A. Friedler |
| 2021 | Shared Independent Component Analysis for Multi-Subject Neuroimaging. Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen |
| 2021 | Sharp Impossibility Results for Hyper-graph Testing. Jiashun Jin, Zheng Tracy Ke, Jiajun Liang |
| 2021 | Shift Invariance Can Reduce Adversarial Robustness. Vasu Singla, Songwei Ge, Ronen Basri, David W. Jacobs |
| 2021 | Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi |
| 2021 | Shifted Chunk Transformer for Spatio-Temporal Representational Learning. Xuefan Zha, Wentao Zhu, Xun Lv, Sen Yang, Ji Liu |
| 2021 | Sifting through the noise: Universal first-order methods for stochastic variational inequalities. Kimon Antonakopoulos, Thomas Pethick, Ali Kavis, Panayotis Mertikopoulos, Volkan Cevher |
| 2021 | Sim and Real: Better Together. Shirli Di-Castro Shashua, Dotan Di Castro, Shie Mannor |
| 2021 | SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement. Heyang Qin, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He |
| 2021 | Similarity and Matching of Neural Network Representations. Adrián Csiszárik, Péter Korösi-Szabó, Ákos K. Matszangosz, Gergely Papp, Dániel Varga |
| 2021 | Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying |
| 2021 | Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta |
| 2021 | Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. Koby Bibas, Meir Feder, Tal Hassner |
| 2021 | SketchGen: Generating Constrained CAD Sketches. Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy J. Mitra, Leonidas J. Guibas, Peter Wonka |
| 2021 | Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs. Yujia Yan, Frank Cwitkowitz, Zhiyao Duan |
| 2021 | Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method. Yifan Chen, Qi Zeng, Heng Ji, Yun Yang |
| 2021 | Slice Sampling Reparameterization Gradients. David M. Zoltowski, Diana Cai, Ryan P. Adams |
| 2021 | Sliced Mutual Information: A Scalable Measure of Statistical Dependence. Ziv Goldfeld, Kristjan H. Greenewald |
| 2021 | Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation. Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang, Yun Fu |
| 2021 | Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. Dominik Stöger, Mahdi Soltanolkotabi |
| 2021 | Smooth Bilevel Programming for Sparse Regularization. Clarice Poon, Gabriel Peyré |
| 2021 | Smooth Normalizing Flows. Jonas Köhler, Andreas Krämer, Frank Noé |
| 2021 | SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness. Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Do-Guk Kim, Jinwoo Shin |
| 2021 | Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. Mher Safaryan, Filip Hanzely, Peter Richtárik |
| 2021 | Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing. Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson |
| 2021 | Soft Calibration Objectives for Neural Networks. Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs |
| 2021 | Solving Graph-based Public Goods Games with Tree Search and Imitation Learning. Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi |
| 2021 | Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent. Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras |
| 2021 | Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter. Ruizhe Qin, Mengying Li, Hu Ding |
| 2021 | Space-time Mixing Attention for Video Transformer. Adrian Bulat, Juan-Manuel Pérez-Rúa, Swathikiran Sudhakaran, Brais Martínez, Georgios Tzimiropoulos |
| 2021 | Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. Yan Sun, Wenjun Xiong, Faming Liang |
| 2021 | Sparse Flows: Pruning Continuous-depth Models. Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus |
| 2021 | Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation. Florian Bernard, Daniel Cremers, Johan Thunberg |
| 2021 | Sparse Spiking Gradient Descent. Nicolas Perez Nieves, Dan F. M. Goodman |
| 2021 | Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space. Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, Kui Jia |
| 2021 | Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu |
| 2021 | Sparse Uncertainty Representation in Deep Learning with Inducing Weights. Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li |
| 2021 | Sparse is Enough in Scaling Transformers. Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, Lukasz Kaiser, Wojciech Gajewski, Henryk Michalewski, Jonni Kanerva |
| 2021 | Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains. Christian Gumbsch, Martin V. Butz, Georg Martius |
| 2021 | Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework. Tengteng Huang, Yifan Sun, Xun Wang, Haotian Yao, Chi Zhang |
| 2021 | Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2021 | Spatio-Temporal Variational Gaussian Processes. Oliver Hamelijnck, William J. Wilkinson, Niki Andreas Lopi, Arno Solin, Theodoros Damoulas |
| 2021 | Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks. Zichen Miao, Ze Wang, Xiuyuan Cheng, Qiang Qiu |
| 2021 | Spectral embedding for dynamic networks with stability guarantees. Ian Gallagher, Andrew Jones, Patrick Rubin-Delanchy |
| 2021 | Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution. Guangpin Tao, Xiaozhong Ji, Wenzhuo Wang, Shuo Chen, Chuming Lin, Yun Cao, Tong Lu, Donghao Luo, Ying Tai |
| 2021 | Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network. Xiaolin Hu, Kai Li, Weiyi Zhang, Yi Luo, Jean-Marie Lemercier, Timo Gerkmann |
| 2021 | Speech-T: Transducer for Text to Speech and Beyond. Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu |
| 2021 | Speedy Performance Estimation for Neural Architecture Search. Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal |
| 2021 | Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay. Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun |
| 2021 | Spot the Difference: Detection of Topological Changes via Geometric Alignment. Steffen Czolbe, Aasa Feragen, Oswin Krause |
| 2021 | Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery. Junhui Zhang, Jingkai Yan, John Wright |
| 2021 | Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel. Dominic Richards, Ilja Kuzborskij |
| 2021 | Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$. Yegor Klochkov, Nikita Zhivotovskiy |
| 2021 | Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang |
| 2021 | Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation. Nicklas Hansen, Hao Su, Xiaolong Wang |
| 2021 | Stabilizing Dynamical Systems via Policy Gradient Methods. Juan C. Perdomo, Jack Umenberger, Max Simchowitz |
| 2021 | Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks. Qiyu Kang, Yang Song, Qinxu Ding, Wee Peng Tay |
| 2021 | Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding. Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu |
| 2021 | Stateful ODE-Nets using Basis Function Expansions. Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | Stateful Strategic Regression. Keegan Harris, Hoda Heidari, Zhiwei Steven Wu |
| 2021 | Statistical Inference with M-Estimators on Adaptively Collected Data. Kelly W. Zhang, Lucas Janson, Susan A. Murphy |
| 2021 | Statistical Query Lower Bounds for List-Decodable Linear Regression. Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart |
| 2021 | Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency. Anish Chakrabarty, Swagatam Das |
| 2021 | Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders. Konstantin Genin |
| 2021 | Statistically and Computationally Efficient Linear Meta-representation Learning. Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh |
| 2021 | Stochastic $L^\natural$-convex Function Minimization. Haixiang Zhang, Zeyu Zheng, Javad Lavaei |
| 2021 | Stochastic Anderson Mixing for Nonconvex Stochastic Optimization. Fuchao Wei, Chenglong Bao, Yang Liu |
| 2021 | Stochastic Bias-Reduced Gradient Methods. Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford |
| 2021 | Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien |
| 2021 | Stochastic Multi-Armed Bandits with Control Variates. Arun Verma, Manjesh Kumar Hanawal |
| 2021 | Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet |
| 2021 | Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang |
| 2021 | Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2021 | Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser. Zahra Kadkhodaie, Eero P. Simoncelli |
| 2021 | Stochastic bandits with groups of similar arms. Fabien Pesquerel, Hassan Saber, Odalric-Ambrym Maillard |
| 2021 | Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees. Joshua Cutler, Dmitriy Drusvyatskiy, Zaïd Harchaoui |
| 2021 | Storchastic: A Framework for General Stochastic Automatic Differentiation. Emile van Krieken, Jakub M. Tomczak, Annette ten Teije |
| 2021 | Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare. Joshua Kavner, Lirong Xia |
| 2021 | Streaming Belief Propagation for Community Detection. Yuchen Wu, Jakab Tardos, MohammadHossein Bateni, André Linhares, Filipe Miguel Gonçalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard |
| 2021 | Streaming Linear System Identification with Reverse Experience Replay. Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli |
| 2021 | Stronger NAS with Weaker Predictors. Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan |
| 2021 | Structural Credit Assignment in Neural Networks using Reinforcement Learning. Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James E. Kostas, Philip S. Thomas, Martha White |
| 2021 | Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam |
| 2021 | Structure-Aware Random Fourier Kernel for Graphs. Jinyuan Fang, Qiang Zhang, Zaiqiao Meng, Shangsong Liang |
| 2021 | Structured Denoising Diffusion Models in Discrete State-Spaces. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg |
| 2021 | Structured Dropout Variational Inference for Bayesian Neural Networks. Son Nguyen, Duong Nguyen, Khai Nguyen, Khoat Than, Hung Bui, Nhat Ho |
| 2021 | Structured Reordering for Modeling Latent Alignments in Sequence Transduction. Bailin Wang, Mirella Lapata, Ivan Titov |
| 2021 | Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training. Anup Sarma, Sonali Singh, Huaipan Jiang, Rui Zhang, Mahmut T. Kandemir, Chita R. Das |
| 2021 | Stylized Dialogue Generation with Multi-Pass Dual Learning. Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu |
| 2021 | Sub-Linear Memory: How to Make Performers SLiM. Valerii Likhosherstov, Krzysztof Marcin Choromanski, Jared Quincy Davis, Xingyou Song, Adrian Weller |
| 2021 | SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning. Talip Ucar, Ehsan Hajiramezanali, Lindsay Edwards |
| 2021 | Subgame solving without common knowledge. Brian Hu Zhang, Tuomas Sandholm |
| 2021 | Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning. Alberto Maria Metelli, Alessio Russo, Marcello Restelli |
| 2021 | Subgoal Search For Complex Reasoning Tasks. Konrad Czechowski, Tomasz Odrzygózdz, Marek Zbysinski, Michal Zawalski, Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski, Piotr Milos |
| 2021 | Subgraph Federated Learning with Missing Neighbor Generation. Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu-Ming Yiu |
| 2021 | Subgroup Generalization and Fairness of Graph Neural Networks. Jiaqi Ma, Junwei Deng, Qiaozhu Mei |
| 2021 | Submodular + Concave. Siddharth Mitra, Moran Feldman, Amin Karbasi |
| 2021 | Subquadratic Overparameterization for Shallow Neural Networks. Chaehwan Song, Ali Ramezani-Kebrya, Thomas Pethick, Armin Eftekhari, Volkan Cevher |
| 2021 | Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning. Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee |
| 2021 | Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer. Junya Chen, Zidi Xiu, Benjamin Goldstein, Ricardo Henao, Lawrence Carin, Chenyang Tao |
| 2021 | Supervising the Transfer of Reasoning Patterns in VQA. Corentin Kervadec, Christian Wolf, Grigory Antipov, Moez Baccouche, Madiha Nadri |
| 2021 | Support Recovery of Sparse Signals from a Mixture of Linear Measurements. Soumyabrata Pal, Arya Mazumdar, Venkata Gandikota |
| 2021 | Support vector machines and linear regression coincide with very high-dimensional features. Navid Ardeshir, Clayton Sanford, Daniel J. Hsu |
| 2021 | Surrogate Regret Bounds for Polyhedral Losses. Rafael M. Frongillo, Bo Waggoner |
| 2021 | SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. Alicia Curth, Changhee Lee, Mihaela van der Schaar |
| 2021 | SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev |
| 2021 | Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen |
| 2021 | Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi |
| 2021 | SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes. Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela M. Wood, Mihaela van der Schaar |
| 2021 | Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls. Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sébastien Lahaie, Miles Lubin, Vahab S. Mirrokni, Jann Spiess, Guido Imbens |
| 2021 | Systematic Generalization with Edge Transformers. Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau |
| 2021 | T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs. Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang |
| 2021 | TAAC: Temporally Abstract Actor-Critic for Continuous Control. Haonan Yu, Wei Xu, Haichao Zhang |
| 2021 | TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework. Shun Lu, Jixiang Li, Jianchao Tan, Sen Yang, Ji Liu |
| 2021 | TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok |
| 2021 | TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness. Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li |
| 2021 | TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi |
| 2021 | TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning. Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli |
| 2021 | Tactical Optimism and Pessimism for Deep Reinforcement Learning. Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan |
| 2021 | Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time. Ferran Alet, Maria Bauzá, Kenji Kawaguchi, Nurullah Giray Kuru, Tomás Lozano-Pérez, Leslie Pack Kaelbling |
| 2021 | Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu |
| 2021 | Targeted Neural Dynamical Modeling. Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig |
| 2021 | Task-Adaptive Neural Network Search with Meta-Contrastive Learning. Wonyong Jeong, Hayeon Lee, Geon Park, Eunyoung Hyung, Jinheon Baek, Sung Ju Hwang |
| 2021 | Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. Dongmin Park, Hwanjun Song, Minseok Kim, Jae-Gil Lee |
| 2021 | Taxonomizing local versus global structure in neural network loss landscapes. Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney |
| 2021 | Teachable Reinforcement Learning via Advice Distillation. Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas |
| 2021 | Teaching an Active Learner with Contrastive Examples. Chaoqi Wang, Adish Singla, Yuxin Chen |
| 2021 | Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm. Akash Kumar, Yuxin Chen, Adish Singla |
| 2021 | Techniques for Symbol Grounding with SATNet. Sever Topan, David Rolnick, Xujie Si |
| 2021 | Temporal-attentive Covariance Pooling Networks for Video Recognition. Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu, Peihua Li |
| 2021 | Temporally Abstract Partial Models. Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup |
| 2021 | Tensor Normal Training for Deep Learning Models. Yi Ren, Donald Goldfarb |
| 2021 | Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity. Gabriel Koch Ocker, Michael A. Buice |
| 2021 | Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs. Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun |
| 2021 | Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization. Yusuke Iwasawa, Yutaka Matsuo |
| 2021 | Test-Time Personalization with a Transformer for Human Pose Estimation. Yizhuo Li, Miao Hao, Zonglin Di, Nitesh B. Gundavarapu, Xiaolong Wang |
| 2021 | Test-time Collective Prediction. Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan |
| 2021 | TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks. Yu Li, Min Li, Qiuxia Lai, Yannan Liu, Qiang Xu |
| 2021 | Testing Probabilistic Circuits. Yash Pote, Kuldeep S. Meel |
| 2021 | The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy. Masahiro Kato, Kenichiro McAlinn, Shota Yasui |
| 2021 | The Benefits of Implicit Regularization from SGD in Least Squares Problems. Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade |
| 2021 | The Causal-Neural Connection: Expressiveness, Learnability, and Inference. Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim |
| 2021 | The Complexity of Bayesian Network Learning: Revisiting the Superstructure. Robert Ganian, Viktoriia Korchemna |
| 2021 | The Complexity of Sparse Tensor PCA. Davin Choo, Tommaso d'Orsi |
| 2021 | The Difficulty of Passive Learning in Deep Reinforcement Learning. Georg Ostrovski, Pablo Samuel Castro, Will Dabney |
| 2021 | The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers. Fabian Latorre, Leello Tadesse Dadi, Paul Rolland, Volkan Cevher |
| 2021 | The Elastic Lottery Ticket Hypothesis. Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, Zhangyang Wang |
| 2021 | The Emergence of Objectness: Learning Zero-shot Segmentation from Videos. Runtao Liu, Zhirong Wu, Stella X. Yu, Stephen Lin |
| 2021 | The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization. Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk |
| 2021 | The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle. Fang Kong, Yueran Yang, Wei Chen, Shuai Li |
| 2021 | The Image Local Autoregressive Transformer. Chenjie Cao, Yuxin Hong, Xiang Li, Chengrong Wang, Chengming Xu, Yanwei Fu, Xiangyang Xue |
| 2021 | The Implicit Bias of Minima Stability: A View from Function Space. Rotem Mulayoff, Tomer Michaeli, Daniel Soudry |
| 2021 | The Inductive Bias of Quantum Kernels. Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf |
| 2021 | The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains. Daron Anderson, Douglas J. Leith |
| 2021 | The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. Geoff Pleiss, John P. Cunningham |
| 2021 | The Limits of Optimal Pricing in the Dark. Quinlan Dawkins, Minbiao Han, Haifeng Xu |
| 2021 | The Many Faces of Adversarial Risk. Muni Sreenivas Pydi, Varun S. Jog |
| 2021 | The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. Peter Hase, Harry Xie, Mohit Bansal |
| 2021 | The Pareto Frontier of model selection for general Contextual Bandits. Teodor Vanislavov Marinov, Julian Zimmert |
| 2021 | The Role of Global Labels in Few-Shot Classification and How to Infer Them. Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto |
| 2021 | The Semi-Random Satisfaction of Voting Axioms. Lirong Xia |
| 2021 | The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. Yujin Tang, David Ha |
| 2021 | The Skellam Mechanism for Differentially Private Federated Learning. Naman Agarwal, Peter Kairouz, Ziyu Liu |
| 2021 | The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation. Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré |
| 2021 | The Utility of Explainable AI in Ad Hoc Human-Machine Teaming. Rohan R. Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew C. Gombolay |
| 2021 | The Value of Information When Deciding What to Learn. Dilip Arumugam, Benjamin Van Roy |
| 2021 | The balancing principle for parameter choice in distance-regularized domain adaptation. Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei V. Pereverzyev, Bernhard Alois Moser |
| 2021 | The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition. Tiancheng Jin, Longbo Huang, Haipeng Luo |
| 2021 | The convergence rate of regularized learning in games: From bandits and uncertainty to optimism and beyond. Angeliki Giannou, Emmanouil V. Vlatakis-Gkaragkounis, Panayotis Mertikopoulos |
| 2021 | The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian. Yu-Chia Chen, Marina Meila |
| 2021 | The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. Giang Nguyen, Daeyoung Kim, Anh Nguyen |
| 2021 | The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake A. Richards |
| 2021 | The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization. Mufan (Bill) Li, Mihai Nica, Daniel M. Roy |
| 2021 | The staircase property: How hierarchical structure can guide deep learning. Emmanuel Abbe, Enric Boix-Adserà, Matthew S. Brennan, Guy Bresler, Dheeraj Nagaraj |
| 2021 | There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist |
| 2021 | Think Big, Teach Small: Do Language Models Distil Occam's Razor? Gonzalo Jaimovitch-López, David Castellano Falcón, César Ferri, José Hernández-Orallo |
| 2021 | Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates. Alp Yurtsever, Alex Gu, Suvrit Sra |
| 2021 | Three-dimensional spike localization and improved motion correction for Neuropixels recordings. Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski |
| 2021 | Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai |
| 2021 | Tighter Expected Generalization Error Bounds via Wasserstein Distance. Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund |
| 2021 | Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods. Seohong Park, Jaekyeom Kim, Gunhee Kim |
| 2021 | Time-independent Generalization Bounds for SGLD in Non-convex Settings. Tyler Farghly, Patrick Rebeschini |
| 2021 | Time-series Generation by Contrastive Imitation. Daniel Jarrett, Ioana Bica, Mihaela van der Schaar |
| 2021 | To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs. Thomas Scialom, Paul-Alexis Dray, Jacopo Staiano, Sylvain Lamprier, Benjamin Piwowarski |
| 2021 | To The Point: Correspondence-driven monocular 3D category reconstruction. Filippos Kokkinos, Iasonas Kokkinos |
| 2021 | ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation. Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen |
| 2021 | TokenLearner: Adaptive Space-Time Tokenization for Videos. Michael S. Ryoo, A. J. Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova |
| 2021 | Topic Modeling Revisited: A Document Graph-based Neural Network Perspective. Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, Hengshu Zhu, Hui Xiong |
| 2021 | TopicNet: Semantic Graph-Guided Topic Discovery. Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou |
| 2021 | Topographic VAEs learn Equivariant Capsules. T. Anderson Keller, Max Welling |
| 2021 | Topological Attention for Time Series Forecasting. Sebastian Zeng, Florian Graf, Christoph D. Hofer, Roland Kwitt |
| 2021 | Topological Detection of Trojaned Neural Networks. Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, Chao Chen |
| 2021 | Topological Relational Learning on Graphs. Yuzhou Chen, Baris Coskunuzer, Yulia R. Gel |
| 2021 | Topology-Imbalance Learning for Semi-Supervised Node Classification. Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, Xu Sun |
| 2021 | Towards Best-of-All-Worlds Online Learning with Feedback Graphs. Liad Erez, Tomer Koren |
| 2021 | Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples. Sungyoon Lee, Woojin Lee, Jinseong Park, Jaewook Lee |
| 2021 | Towards Biologically Plausible Convolutional Networks. Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham |
| 2021 | Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective. Zhengzhuo Xu, Zenghao Chai, Chun Yuan |
| 2021 | Towards Context-Agnostic Learning Using Synthetic Data. Charles Jin, Martin C. Rinard |
| 2021 | Towards Deeper Deep Reinforcement Learning with Spectral Normalization. Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger |
| 2021 | Towards Efficient and Effective Adversarial Training. Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R. |
| 2021 | Towards Enabling Meta-Learning from Target Models. Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan |
| 2021 | Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond. Risheng Liu, Yaohua Liu, Shangzhi Zeng, Jin Zhang |
| 2021 | Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning. Siyuan Zhang, Nan Jiang |
| 2021 | Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. Ming Yin, Yu-Xiang Wang |
| 2021 | Towards Lower Bounds on the Depth of ReLU Neural Networks. Christoph Hertrich, Amitabh Basu, Marco Di Summa, Martin Skutella |
| 2021 | Towards Multi-Grained Explainability for Graph Neural Networks. Xiang Wang, Ying-Xin Wu, An Zhang, Xiangnan He, Tat-Seng Chua |
| 2021 | Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. Qitian Wu, Chenxiao Yang, Junchi Yan |
| 2021 | Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation. David Acuna, Jonah Philion, Sanja Fidler |
| 2021 | Towards Robust Bisimulation Metric Learning. Mete Kemertas, Tristan Aumentado-Armstrong |
| 2021 | Towards Robust and Reliable Algorithmic Recourse. Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju |
| 2021 | Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett |
| 2021 | Towards Sample-efficient Overparameterized Meta-learning. Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel |
| 2021 | Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN. Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang |
| 2021 | Towards Sharper Generalization Bounds for Structured Prediction. Shaojie Li, Yong Liu |
| 2021 | Towards Stable and Robust AdderNets. Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu |
| 2021 | Towards Tight Communication Lower Bounds for Distributed Optimisation. Janne H. Korhonen, Dan Alistarh |
| 2021 | Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization. Jianhao Wang, Zhizhou Ren, Beining Han, Jianing Ye, Chongjie Zhang |
| 2021 | Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond. Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan |
| 2021 | Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games. Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu, Yaodong Yang |
| 2021 | Towards a Theoretical Framework of Out-of-Distribution Generalization. Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang |
| 2021 | Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang |
| 2021 | Towards a Unified Information-Theoretic Framework for Generalization. Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy |
| 2021 | Towards mental time travel: a hierarchical memory for reinforcement learning agents. Andrew K. Lampinen, Stephanie C. Y. Chan, Andrea Banino, Felix Hill |
| 2021 | Towards optimally abstaining from prediction with OOD test examples. Adam Kalai, Varun Kanade |
| 2021 | Towards robust vision by multi-task learning on monkey visual cortex. Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George H. Denfield, Andreas S. Tolias, Fabian H. Sinz |
| 2021 | Towards understanding retrosynthesis by energy-based models. Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai |
| 2021 | Tracking People with 3D Representations. Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik |
| 2021 | Tracking Without Re-recognition in Humans and Machines. Drew Linsley, Girik Malik, Junkyung Kim, Lakshmi Narasimhan Govindarajan, Ennio Mingolla, Thomas Serre |
| 2021 | Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. Brendan Leigh Ross, Jesse C. Cresswell |
| 2021 | Tractable Regularization of Probabilistic Circuits. Anji Liu, Guy Van den Broeck |
| 2021 | Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar |
| 2021 | Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin |
| 2021 | Training Neural Networks is ER-complete. Mikkel Abrahamsen, Linda Kleist, Tillmann Miltzow |
| 2021 | Training Neural Networks with Fixed Sparse Masks. Yi-Lin Sung, Varun Nair, Colin Raffel |
| 2021 | Training Over-parameterized Models with Non-decomposable Objectives. Harikrishna Narasimhan, Aditya Krishna Menon |
| 2021 | Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time. Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi |
| 2021 | TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up. Yifan Jiang, Shiyu Chang, Zhangyang Wang |
| 2021 | TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification. Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, Yongbing Zhang |
| 2021 | TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification. Shengcai Liao, Ling Shao |
| 2021 | Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization. Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han |
| 2021 | Transformer in Transformer. Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang |
| 2021 | TransformerFusion: Monocular RGB Scene Reconstruction using Transformers. Aljaz Bozic, Pablo R. Palafox, Justus Thies, Angela Dai, Matthias Nießner |
| 2021 | Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs. Jinwoo Kim, Saeyoon Oh, Seunghoon Hong |
| 2021 | Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation. Qiming Hu, Xiaojie Guo |
| 2021 | Tree in Tree: from Decision Trees to Decision Graphs. Bingzhao Zhu, Mahsa Shoaran |
| 2021 | TriBERT: Human-centric Audio-visual Representation Learning. Tanzila Rahman, Mengyu Yang, Leonid Sigal |
| 2021 | True Few-Shot Learning with Language Models. Ethan Perez, Douwe Kiela, Kyunghyun Cho |
| 2021 | Truncated Marginal Neural Ratio Estimation. Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger |
| 2021 | Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions. Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu |
| 2021 | Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer. Ge Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao |
| 2021 | Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL. Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J. Roberts |
| 2021 | Turing Completeness of Bounded-Precision Recurrent Neural Networks. Stephen Chung, Hava T. Siegelmann |
| 2021 | Twice regularized MDPs and the equivalence between robustness and regularization. Esther Derman, Matthieu Geist, Shie Mannor |
| 2021 | Twins: Revisiting the Design of Spatial Attention in Vision Transformers. Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen |
| 2021 | Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. Amrith Setlur, Oscar Li, Virginia Smith |
| 2021 | Two steps to risk sensitivity. Chris Gagne, Peter Dayan |
| 2021 | Two-sided fairness in rankings via Lorenz dominance. Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier |
| 2021 | Two-step lookahead Bayesian optimization with inequality constraints. Yunxiang Zhang, Xiangyu Zhang, Peter I. Frazier |
| 2021 | TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis. Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt, James Tompkin, Matthew O'Toole |
| 2021 | UCB-based Algorithms for Multinomial Logistic Regression Bandits. Sanae Amani, Christos Thrampoulidis |
| 2021 | UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis. Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang |
| 2021 | USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems. Guangmo Tong |
| 2021 | Ultrahyperbolic Neural Networks. Marc T. Law |
| 2021 | Unadversarial Examples: Designing Objects for Robust Vision. Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor |
| 2021 | Unbalanced Optimal Transport through Non-negative Penalized Linear Regression. Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, Gilles Gasso |
| 2021 | Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning. Youngkyu Hong, Eunho Yang |
| 2021 | Uncertain Decisions Facilitate Better Preference Learning. Cassidy Laidlaw, Stuart Russell |
| 2021 | Uncertainty Calibration for Ensemble-Based Debiasing Methods. Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Cheng, Zhi-Ming Ma, Yanyan Lan |
| 2021 | Uncertainty Quantification and Deep Ensembles. Rahul Rahaman, Alexandre H. Thiéry |
| 2021 | Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble. Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song |
| 2021 | Uncertainty-Driven Loss for Single Image Super-Resolution. Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi |
| 2021 | Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems. Menoua Keshishian, Samuel Norman-Haignere, Nima Mesgarani |
| 2021 | Understanding Bandits with Graph Feedback. Houshuang Chen, Zengfeng Huang, Shuai Li, Chihao Zhang |
| 2021 | Understanding Deflation Process in Over-parametrized Tensor Decomposition. Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou |
| 2021 | Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling |
| 2021 | Understanding How Encoder-Decoder Architectures Attend. Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan |
| 2021 | Understanding Instance-based Interpretability of Variational Auto-Encoders. Zhifeng Kong, Kamalika Chaudhuri |
| 2021 | Understanding Interlocking Dynamics of Cooperative Rationalization. Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola |
| 2021 | Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. Kento Nozawa, Issei Sato |
| 2021 | Understanding Partial Multi-Label Learning via Mutual Information. Xiuwen Gong, Dong Yuan, Wei Bao |
| 2021 | Understanding and Improving Early Stopping for Learning with Noisy Labels. Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu |
| 2021 | Understanding the Effect of Stochasticity in Policy Optimization. Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans |
| 2021 | Understanding the Generalization Benefit of Model Invariance from a Data Perspective. Sicheng Zhu, Bang An, Furong Huang |
| 2021 | Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm |
| 2021 | Understanding the Under-Coverage Bias in Uncertainty Estimation. Yu Bai, Song Mei, Huan Wang, Caiming Xiong |
| 2021 | Unfolding Taylor's Approximations for Image Restoration. Man Zhou, Xueyang Fu, Zeyu Xiao, Gang Yang, Aiping Liu, Zhiwei Xiong |
| 2021 | UniDoc: Unified Pretraining Framework for Document Understanding. Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Rajiv Jain, Nikolaos Barmpalios, Ani Nenkova, Tong Sun |
| 2021 | Uniform Concentration Bounds toward a Unified Framework for Robust Clustering. Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Q. Xu |
| 2021 | Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro |
| 2021 | Uniform Sampling over Episode Difficulty. Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto |
| 2021 | Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko |
| 2021 | Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. Deeksha Adil, Brian Bullins, Sushant Sachdeva |
| 2021 | Unifying lower bounds on prediction dimension of convex surrogates. Jessica Finocchiaro, Rafael M. Frongillo, Bo Waggoner |
| 2021 | Unintended Selection: Persistent Qualification Rate Disparities and Interventions. Reilly Raab, Yang Liu |
| 2021 | Unique sparse decomposition of low rank matrices. Dian Jin, Xin Bing, Yuqian Zhang |
| 2021 | Universal Approximation Using Well-Conditioned Normalizing Flows. Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski |
| 2021 | Universal Graph Convolutional Networks. Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, Jiawei Han |
| 2021 | Universal Off-Policy Evaluation. Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas |
| 2021 | Universal Rate-Distortion-Perception Representations for Lossy Compression. George Zhang, Jingjing Qian, Jun Chen, Ashish Khisti |
| 2021 | Universal Semi-Supervised Learning. Zhuo Huang, Chao Xue, Bo Han, Jian Yang, Chen Gong |
| 2021 | Unlabeled Principal Component Analysis. Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris |
| 2021 | Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning. Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng |
| 2021 | Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning. Jinxin Liu, Hao Shen, Donglin Wang, Yachen Kang, Qiangxing Tian |
| 2021 | Unsupervised Foreground Extraction via Deep Region Competition. Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu |
| 2021 | Unsupervised Learning of Compositional Energy Concepts. Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, Igor Mordatch |
| 2021 | Unsupervised Motion Representation Learning with Capsule Autoencoders. Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S. Kankanhalli |
| 2021 | Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport. Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao |
| 2021 | Unsupervised Object-Based Transition Models For 3D Partially Observable Environments. Antonia Creswell, Rishabh Kabra, Christopher P. Burgess, Murray Shanahan |
| 2021 | Unsupervised Object-Level Representation Learning from Scene Images. Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy |
| 2021 | Unsupervised Part Discovery from Contrastive Reconstruction. Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi |
| 2021 | Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly. Hee Min Choi, Hyoa Kang, Dokwan Oh |
| 2021 | Unsupervised Speech Recognition. Alexei Baevski, Wei-Ning Hsu, Alexis Conneau, Michael Auli |
| 2021 | User-Level Differentially Private Learning via Correlated Sampling. Badih Ghazi, Ravi Kumar, Pasin Manurangsi |
| 2021 | Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks. Giora Simchoni, Saharon Rosset |
| 2021 | VAST: Value Function Factorization with Variable Agent Sub-Teams. Thomy Phan, Fabian Ritz, Lenz Belzner, Philipp Altmann, Thomas Gabor, Claudia Linnhoff-Popien |
| 2021 | VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text. Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong |
| 2021 | VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein |
| 2021 | Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory. Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou |
| 2021 | Validation Free and Replication Robust Volume-based Data Valuation. Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low |
| 2021 | Variance-Aware Off-Policy Evaluation with Linear Function Approximation. Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu |
| 2021 | Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems. Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu |
| 2021 | Variational Bayesian Optimistic Sampling. Brendan O'Donoghue, Tor Lattimore |
| 2021 | Variational Bayesian Reinforcement Learning with Regret Bounds. Brendan O'Donoghue |
| 2021 | Variational Continual Bayesian Meta-Learning. Qiang Zhang, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang, Emine Yilmaz |
| 2021 | Variational Inference for Continuous-Time Switching Dynamical Systems. Lukas Köhs, Bastian Alt, Heinz Koeppl |
| 2021 | Variational Model Inversion Attacks. Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani |
| 2021 | Variational Multi-Task Learning with Gumbel-Softmax Priors. Jiayi Shen, Xiantong Zhen, Marcel Worring, Ling Shao |
| 2021 | Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices. Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard |
| 2021 | Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth |
| 2021 | ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction. Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu, Deva Ramanan |
| 2021 | ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias. Yufei Xu, Qiming Zhang, Jing Zhang, Dacheng Tao |
| 2021 | VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer. Zineng Tang, Jaemin Cho, Hao Tan, Mohit Bansal |
| 2021 | Video Instance Segmentation using Inter-Frame Communication Transformers. Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim |
| 2021 | VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media. Yizhou Zhang, Karishma Sharma, Yan Liu |
| 2021 | Visual Adversarial Imitation Learning using Variational Models. Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn |
| 2021 | Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman |
| 2021 | Visualizing the Emergence of Intermediate Visual Patterns in DNNs. Mingjie Li, Shaobo Wang, Quanshi Zhang |
| 2021 | VoiceMixer: Adversarial Voice Style Mixup. Sang-Hoon Lee, Ji-Hoon Kim, Hyunseung Chung, Seong-Whan Lee |
| 2021 | Volume Rendering of Neural Implicit Surfaces. Lior Yariv, Jiatao Gu, Yoni Kasten, Yaron Lipman |
| 2021 | Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image. Feng Liu, Xiaoming Liu |
| 2021 | Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang |
| 2021 | Weak-shot Fine-grained Classification via Similarity Transfer. Junjie Chen, Li Niu, Liu Liu, Liqing Zhang |
| 2021 | Weighted model estimation for offline model-based reinforcement learning. Toru Hishinuma, Kei Senda |
| 2021 | Weisfeiler and Lehman Go Cellular: CW Networks. Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein |
| 2021 | Well-tuned Simple Nets Excel on Tabular Datasets. Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka |
| 2021 | What Makes Multi-Modal Learning Better than Single (Provably). Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang |
| 2021 | What Matters for Adversarial Imitation Learning? Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz |
| 2021 | What can linearized neural networks actually say about generalization? Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard |
| 2021 | What training reveals about neural network complexity. Andreas Loukas, Marinos Poiitis, Stefanie Jegelka |
| 2021 | What's a good imputation to predict with missing values? Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux |
| 2021 | When Are Solutions Connected in Deep Networks? Quynh Nguyen, Pierre Bréchet, Marco Mondelli |
| 2021 | When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work. Jiawei Zhang, Yushun Zhang, Mingyi Hong, Ruoyu Sun, Zhi-Quan Luo |
| 2021 | When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking. Peisong Wen, Qianqian Xu, Zhiyong Yang, Yuan He, Qingming Huang |
| 2021 | When Is Generalizable Reinforcement Learning Tractable? Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar |
| 2021 | When Is Unsupervised Disentanglement Possible? Daniella Horan, Eitan Richardson, Yair Weiss |
| 2021 | When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Chuang Gan |
| 2021 | When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash |
| 2021 | Which Mutual-Information Representation Learning Objectives are Sufficient for Control? Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine |
| 2021 | Who Leads and Who Follows in Strategic Classification? Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan |
| 2021 | Why Do Better Loss Functions Lead to Less Transferable Features? Simon Kornblith, Ting Chen, Honglak Lee, Mohammad Norouzi |
| 2021 | Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. Colin Wei, Sang Michael Xie, Tengyu Ma |
| 2021 | Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine |
| 2021 | Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks. Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong |
| 2021 | Why Spectral Normalization Stabilizes GANs: Analysis and Improvements. Zinan Lin, Vyas Sekar, Giulia Fanti |
| 2021 | Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation. Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang, Subbarao Kambhampati |
| 2021 | Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark. Stefan O'Toole, Nir Lipovetzky, Miquel Ramírez, Adrian R. Pearce |
| 2021 | Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences. Grant Schoenebeck, Biaoshuai Tao |
| 2021 | Word2Fun: Modelling Words as Functions for Diachronic Word Representation. Benyou Wang, Emanuele Di Buccio, Massimo Melucci |
| 2021 | XCiT: Cross-Covariance Image Transformers. Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Hervé Jégou |
| 2021 | XDO: A Double Oracle Algorithm for Extensive-Form Games. Stephen McAleer, John B. Lanier, Kevin A. Wang, Pierre Baldi, Roy Fox |
| 2021 | You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism. Weijie J. Su |
| 2021 | You Never Cluster Alone. Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip H. S. Torr, Ling Shao |
| 2021 | You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection. Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu |
| 2021 | You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership. Xuxi Chen, Tianlong Chen, Zhenyu Zhang, Zhangyang Wang |
| 2021 | Your head is there to move you around: Goal-driven models of the primate dorsal pathway. Patrick J. Mineault, Shahab Bakhtiari, Blake A. Richards, Christopher C. Pack |
| 2021 | Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. Maciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski |
| 2021 | argmax centroid. Chengyue Gong, Mao Ye, Qiang Liu |
| 2021 | iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder. Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li |