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Improving Out-of-Distribution Robustness via Selective Augmentation


Jan 02, 2022
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn


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Scaffolding Sets


Nov 17, 2021
Maya Burhanpurkar, Zhun Deng, Cynthia Dwork, Linjun Zhang

* 32 pages, 4 figures 

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The Power of Contrast for Feature Learning: A Theoretical Analysis


Oct 06, 2021
Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang

* 44 pages 

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Understanding Dynamics of Nonlinear Representation Learning and Its Application


Jun 28, 2021
Kenji Kawaguchi, Linjun Zhang, Zhun Deng


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Adversarial Training Helps Transfer Learning via Better Representations


Jun 18, 2021
Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou


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Meta-Learning with Fewer Tasks through Task Interpolation


Jun 04, 2021
Huaxiu Yao, Linjun Zhang, Chelsea Finn


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High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees


Apr 01, 2021
Zhe Zhang, Linjun Zhang

* 45 pages, 3 figures 

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A Central Limit Theorem for Differentially Private Query Answering


Mar 15, 2021
Jinshuo Dong, Weijie J. Su, Linjun Zhang


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When and How Mixup Improves Calibration


Feb 11, 2021
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou


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The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds


Nov 08, 2020
T. Tony Cai, Yichen Wang, Linjun Zhang

* 43 pages, 6 figures 

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Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression


Nov 06, 2020
Linjun Zhang, Rong Ma, T. Tony Cai, Hongzhe Li


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How Does Mixup Help With Robustness and Generalization?


Oct 09, 2020
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou


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Interpreting Robust Optimization via Adversarial Influence Functions


Oct 03, 2020
Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang


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A Lightweight Algorithm to Uncover Deep Relationships in Data Tables


Sep 07, 2020
Jin Cao, Yibo Zhao, Linjun Zhang, Jason Li

* 9 pages, 4 figures, paper presented on AutoML 2019 (The Third International Workshop on Automation in Machine Learning) 

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Improving Adversarial Robustness via Unlabeled Out-of-Domain Data


Jun 15, 2020
Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou


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The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy


Feb 12, 2019
T. Tony Cai, Yichen Wang, Linjun Zhang

* 34 pages, 6 figures 

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A Sparse PCA Approach to Clustering


Feb 16, 2016
T. Tony Cai, Linjun Zhang

* This paper is part of a discussion of the paper "Important feature PCA for high dimensional clustering" by Jiashun Jin and Wanjie Wang to appear in The Annals of Statistics 

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