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ORCID24th AISTATS 2021: Virtual Event
- Arindam Banerjee, Kenji Fukumizu:
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event. Proceedings of Machine Learning Research 130, PMLR 2021 - Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On the Effect of Auxiliary Tasks on Representation Dynamics. 1-9 - Ismael Lemhadri, Feng Ruan, Robert Tibshirani:
LassoNet: Neural Networks with Feature Sparsity. 10-18 - Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta:
Projection-Free Optimization on Uniformly Convex Sets. 19-27 - Shinsaku Sakaue:
Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications. 28-36 - Antoine Wehenkel, Gilles Louppe:
Graphical Normalizing Flows. 37-45 - Yajie Bao
, Weijia Xiong:
One-Round Communication Efficient Distributed M-Estimation. 46-54 - Valerii Likhosherstov, Jared Davis, Krzysztof Choromanski, Adrian Weller:
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices. 55-63 - Hisham Husain, Kamil Ciosek, Ryota Tomioka:
Regularized Policies are Reward Robust. 64-72 - Taihong Xiao, Xin-Yu Zhang, Hao-Lin Jia, Ming-Ming Cheng, Ming-Hsuan Yang:
Semi-Supervised Learning with Meta-Gradient. 73-81 - Sattar Vakili, Kia Khezeli, Victor Picheny:
On Information Gain and Regret Bounds in Gaussian Process Bandits. 82-90 - Daniel Hsu, Vidya Muthukumar, Ji Xu:
On the proliferation of support vectors in high dimensions. 91-99 - Nikhil Mehta, Kevin J. Liang, Vinay Kumar Verma, Lawrence Carin:
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors. 100-108 - Elchanan Solomon, Alexander Wagner, Paul Bendich:
A Fast and Robust Method for Global Topological Functional Optimization. 109-117 - Sida Peng, Yang Ning:
Regression Discontinuity Design under Self-selection. 118-126 - Ziping Xu, Amirhossein Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. 127-135 - Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. 136-144 - Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima:
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. 145-153 - Naoto Ohsaka:
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. 154-162 - Lu Yu, Tobias Kaufmann, Johannes Lederer:
False Discovery Rates in Biological Networks. 163-171 - Horace Pan, Risi Kondor:
Fourier Bases for Solving Permutation Puzzles. 172-180 - Feynman T. Liang, Nimar S. Arora, Nazanin Khosravani Tehrani, Yucen Lily Li, Michael Tingley, Erik Meijer:
Accelerating Metropolis-Hastings with Lightweight Inference Compilation. 181-189 - Xing Han, Sambarta Dasgupta, Joydeep Ghosh:
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series. 190-198 - Jiaqi Yang:
Fully Gap-Dependent Bounds for Multinomial Logit Bandit. 199-207 - Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn:
Alternating Direction Method of Multipliers for Quantization. 208-216 - Yuantong Li, Chi-Hua Wang, Guang Cheng:
Online Forgetting Process for Linear Regression Models. 217-225 - Emanuele Dolera, Stefano Favaro, Stefano Peluchetti:
A Bayesian nonparametric approach to count-min sketch under power-law data streams. 226-234 - Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc:
Nonlinear Functional Output Regression: A Dictionary Approach. 235-243 - Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
When MAML Can Adapt Fast and How to Assist When It Cannot. 244-252 - Xavier Gitiaux, Huzefa Rangwala:
Learning Smooth and Fair Representations. 253-261 - Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. 262-270 - Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai:
Contextual Blocking Bandits. 271-279 - Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. 280-288 - HaiYing Wang, Jiahui Zou:
A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling. 289-297 - Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. 298-306 - Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlas, Johannes Rausch, Ce Zhang, Andreas Krause:
Online Active Model Selection for Pre-trained Classifiers. 307-315 - Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. 316-324 - Ting-Han Fan, Peter J. Ramadge:
A Contraction Approach to Model-based Reinforcement Learning. 325-333 - Tomohiro Hayase, Ryo Karakida:
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry. 334-342 - Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke:
Benchmarking Simulation-Based Inference. 343-351 - Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. 352-360 - Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis:
Deep Spectral Ranking. 361-369 - Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou:
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits. 370-378 - Yingjie Bi, Javad Lavaei:
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems. 379-387 - Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. 388-396 - Marco Mondelli, Ramji Venkataramanan:
Approximate Message Passing with Spectral Initialization for Generalized Linear Models. 397-405 - Weishi Shi, Qi Yu:
Active Learning with Maximum Margin Sparse Gaussian Processes. 406-414 - Wenkai Xu, Gesine Reinert:
A Stein Goodness-of-test for Exponential Random Graph Models. 415-423 - François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz:
The Sample Complexity of Level Set Approximation. 424-432 - Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes:
Curriculum Learning by Optimizing Learning Dynamics. 433-441 - Ugo Tanielian, Gérard Biau:
Approximating Lipschitz continuous functions with GroupSort neural networks. 442-450 - Daniel Augusto de Souza, Diego P. P. Mesquita, João Paulo Pordeus Gomes, César Lincoln C. Mattos:
Learning GPLVM with arbitrary kernels using the unscented transformation. 451-459 - Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. 460-468 - Kai Brügge, Asja Fischer, Christian Igel:
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. 469-477 - Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. 478-486 - Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác:
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. 487-495 - Yue Xing, Qifan Song, Guang Cheng:
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. 496-504 - Yue Xing, Qifan Song, Guang Cheng:
On the Generalization Properties of Adversarial Training. 505-513 - Yue Xing, Ruizhi Zhang, Guang Cheng:
Adversarially Robust Estimate and Risk Analysis in Linear Regression. 514-522 - Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári:
Adaptive Approximate Policy Iteration. 523-531 - Guillaume Ausset, Stéphan Clémençon, François Portier:
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. 532-540 - Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. 541-549 - Damien Scieur, Lewis Liu, Thomas Pumir, Nicolas Boumal:
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates. 550-558 - Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar:
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering. 559-567 - Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang:
Generalization Bounds for Stochastic Saddle Point Problems. 568-576 - Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defend by Learning to Attack. 577-585 - Benwei Shi, Jeff M. Phillips:
A Deterministic Streaming Sketch for Ridge Regression. 586-594 - Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao:
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems. 595-603 - Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy:
On the role of data in PAC-Bayes. 604-612 - Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin:
CADA: Communication-Adaptive Distributed Adam. 613-621 - Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna P. Jagannathan:
Bandit algorithms: Letting go of logarithmic regret for statistical robustness. 622-630 - Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf:
Geometrically Enriched Latent Spaces. 631-639 - Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári:
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. 640-648 - Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimensions: Refined analysis beyond double descent. 649-657 - Yoan Russac, Louis Faury, Olivier Cappé, Aurélien Garivier:
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting. 658-666 - Lucas Cassano, Ali H. Sayed:
Logical Team Q-learning: An approach towards factored policies in cooperative MARL. 667-675 - Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven:
Automatic structured variational inference. 676-684 - Victor Garcia Satorras, Max Welling:
Neural Enhanced Belief Propagation on Factor Graphs. 685-693 - Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. 694-702 - Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural nets via local linearization. 703-711 - Samir Chowdhury, Tom Needham:
Generalized Spectral Clustering via Gromov-Wasserstein Learning. 712-720 - Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. 721-729 - David Eriksson, Matthias Poloczek:
Scalable Constrained Bayesian Optimization. 730-738 - Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner:
Sample efficient learning of image-based diagnostic classifiers via probabilistic labels. 739-747 - Jason M. Klusowski, Peter M. Tian:
Nonparametric Variable Screening with Optimal Decision Stumps. 748-756 - Jason M. Klusowski:
Sharp Analysis of a Simple Model for Random Forests. 757-765 - Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele:
Nested Barycentric Coordinate System as an Explicit Feature Map. 766-774 - Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien:
An Analysis of the Adaptation Speed of Causal Models. 775-783 - Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. 784-792 - Yongchan Kwon, Manuel A. Rivas, James Zou:
Efficient Computation and Analysis of Distributional Shapley Values. 793-801 - John C. Duchi, Feng Ruan:
A constrained risk inequality for general losses. 802-810 - Tengyu Xu, Yingbin Liang:
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. 811-819 - Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang:
Learning Prediction Intervals for Regression: Generalization and Calibration. 820-828 - Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng:
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. 829-837 - Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. 838-846 - Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. 847-855 - Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie:
Deep Fourier Kernel for Self-Attentive Point Processes. 856-864 - Matthew Holland:
Robustness and scalability under heavy tails, without strong convexity. 865-873 - Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin:
Understanding the wiring evolution in differentiable neural architecture search. 874-882 - Zhiyu Zhang, Ioannis Ch. Paschalidis:
Provable Hierarchical Imitation Learning via EM. 883-891 - Matthew J. Holland, El Mehdi Haress:
Learning with risk-averse feedback under potentially heavy tails. 892-900 - Vo Nguyen Le Duy, Ichiro Takeuchi:
Parametric Programming Approach for More Powerful and General Lasso Selective Inference. 901-909 - Yanjun Han:
On the High Accuracy Limitation of Adaptive Property Estimation. 910-918 - Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. 919-927 - Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei:
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. 928-936 - Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. 937-945 - Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. 946-954 - Shunsuke Horii:
Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions. 955-963 - Francesco Quinzan, Vanja Doskoc, Andreas Göbel, Tobias Friedrich:
Adaptive Sampling for Fast Constrained Maximization of Submodular Functions. 964-972 - Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi:
Mean-Variance Analysis in Bayesian Optimization under Uncertainty. 973-981 - Fan Wu, Patrick Rebeschini:
Hadamard Wirtinger Flow for Sparse Phase Retrieval. 982-990 - Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett:
Stochastic Linear Bandits Robust to Adversarial Attacks. 991-999 - Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike:
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning. 1000-1008 - Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho:
Optimizing Percentile Criterion using Robust MDPs. 1009-1017 - Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. 1018-1026 - Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates:
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. 1027-1035 - Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. 1036-1044 - Mohamed El Amine Seddik, Cosme Louart, Romain Couillet, Mohamed Tamaazousti:
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. 1045-1053 - Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates:
Measure Transport with Kernel Stein Discrepancy. 1054-1062 - Chuanhao Li, Qingyun Wu, Hongning Wang:
Unifying Clustered and Non-stationary Bandits. 1063-1071 - Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier:
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. 1072-1080 - Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas:
Transforming Gaussian Processes With Normalizing Flows. 1081-1089 - Markus Lange-Hegermann:
Linearly Constrained Gaussian Processes with Boundary Conditions. 1090-1098 - Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic:
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. 1099-1107 - Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez:
Top-m identification for linear bandits. 1108-1116 - Ziwei Guan, Tengyu Xu, Yingbin Liang:
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. 1117-1125 - Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. 1126-1134 - Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
Consistent k-Median: Simpler, Better and Robust. 1135-1143 - Min Wen, Osbert Bastani, Ufuk Topcu:
Algorithms for Fairness in Sequential Decision Making. 1144-1152 - Abhin Shah, Devavrat Shah, Gregory W. Wornell:
On Learning Continuous Pairwise Markov Random Fields. 1153-1161