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Yu-Xiang Wang
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Improved Differentially Private Regression via Gradient Boosting


Mar 06, 2023
Shuai Tang, Sergul Aydore, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu

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No-Regret Linear Bandits beyond Realizability


Feb 26, 2023
Chong Liu, Ming Yin, Yu-Xiang Wang

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Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs


Feb 24, 2023
Dan Qiao, Ming Yin, Yu-Xiang Wang

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* 25 pages 
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Protecting Language Generation Models via Invisible Watermarking


Feb 06, 2023
Xuandong Zhao, Yu-Xiang Wang, Lei Li

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Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy


Dec 31, 2022
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang

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Near-Optimal Differentially Private Reinforcement Learning


Dec 09, 2022
Dan Qiao, Yu-Xiang Wang

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* 38 pages 
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Offline Reinforcement Learning with Closed-Form Policy Improvement Operators


Nov 29, 2022
Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang

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Global Optimization with Parametric Function Approximation


Nov 16, 2022
Chong Liu, Yu-Xiang Wang

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Distillation-Resistant Watermarking for Model Protection in NLP


Oct 07, 2022
Xuandong Zhao, Lei Li, Yu-Xiang Wang

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Differentially Private Bias-Term only Fine-tuning of Foundation Models


Oct 04, 2022
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

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