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Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization


Apr 27, 2021
Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan

* The first three authors contributed equally to this work; 37 pages, 20 figures 

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Multi-Source Causal Inference Using Control Variates


Mar 30, 2021
Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan


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On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective


Mar 27, 2021
Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry


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A Variational Inequality Approach to Bayesian Regression Games


Mar 24, 2021
Wenshuo Guo, Michael I. Jordan, Tianyi Lin


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Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data


Mar 05, 2021
Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan

* 30 pages,9 figures 

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Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors


Feb 13, 2021
Xiaowu Dai, Michael I. Jordan


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Private Prediction Sets


Feb 11, 2021
Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan

* Code available at https://github.com/aangelopoulos/private_prediction_sets 

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Distribution-Free, Risk-Controlling Prediction Sets


Jan 30, 2021
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan

* Project website available at http://www.angelopoulos.ai/blog/posts/rcps/ and codebase available at https://github.com/aangelopoulos/rcps 

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Bandit Learning in Decentralized Matching Markets


Dec 31, 2020
Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan

* 23 pages 

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Stochastic Approximation for Online Tensorial Independent Component Analysis


Dec 28, 2020
Chris Junchi Li, Michael I. Jordan


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Optimal Mean Estimation without a Variance


Dec 08, 2020
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan

* Fixed typographical errors in Theorem 1.2, Lemmas 4.3 and C.8 

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Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations


Nov 09, 2020
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan

* 76 pages. The short version of this work appears in NeurIPS 2020 

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Do Offline Metrics Predict Online Performance in Recommender Systems?


Nov 07, 2020
Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan


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Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization


Oct 31, 2020
Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan


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Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism


Oct 31, 2020
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez

* Preprint. Under review 

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Learning Strategies in Decentralized Matching Markets under Uncertain Preferences


Oct 29, 2020
Xiaowu Dai, Michael I. Jordan


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Uncertainty Sets for Image Classifiers using Conformal Prediction


Sep 29, 2020
Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, Michael I. Jordan

* Codebase available at https://github.com/aangelopoulos/conformal_classification 

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Learning from eXtreme Bandit Feedback


Sep 27, 2020
Romain Lopez, Inderjit Dhillon, Michael I. Jordan


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Exploration in two-stage recommender systems


Sep 01, 2020
Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus

* Published at the REVEAL 2020 workshop (RecSys 2020) 

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ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm


Aug 28, 2020
Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan


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On Localized Discrepancy for Domain Adaptation


Aug 14, 2020
Yuchen Zhang, Mingsheng Long, Jianmin Wang, Michael I. Jordan


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Transferable Calibration with Lower Bias and Variance in Domain Adaptation


Jul 16, 2020
Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan


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Optimal Robust Linear Regression in Nearly Linear Time


Jul 16, 2020
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett


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Manifold Learning via Manifold Deflation


Jul 07, 2020
Daniel Ting, Michael I. Jordan


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Accelerated Message Passing for Entropy-Regularized MAP Inference


Jul 01, 2020
Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett, Michael I. Jordan


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Projection Robust Wasserstein Distance and Riemannian Optimization


Jun 28, 2020
Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan

* The first two authors contributed equally 

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On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification


Jun 26, 2020
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan

* Correct some typos; 46 Pages, 41 figures 

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