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Baihe Huang
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2020 – today
- 2024
- [c10]Baihe Huang, Hiteshi Sharma, Yi Mao:
Enhancing Language Model Alignment: A Confidence-Based Approach to Label Smoothing. EMNLP 2024: 21341-21352 - [c9]Hanlin Zhu, Baihe Huang, Stuart Russell:
On Representation Complexity of Model-based and Model-free Reinforcement Learning. ICLR 2024 - [i16]Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar:
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR abs/2403.13893 (2024) - [i15]Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell:
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR abs/2405.04669 (2024) - [i14]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. CoRR abs/2406.19617 (2024) - 2023
- [j1]Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen
, Jason D. Lee, Yuejie Chi
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. SIAM J. Optim. 33(2): 1061-1091 (2023) - [c8]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. AISTATS 2023: 6806-6821 - [c7]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. NeurIPS 2023 - [i13]Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan:
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR abs/2306.05592 (2023) - [i12]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. CoRR abs/2306.12383 (2023) - [i11]Hanlin Zhu, Baihe Huang, Stuart Russell:
On Representation Complexity of Model-based and Model-free Reinforcement Learning. CoRR abs/2310.01706 (2023) - [i10]Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan:
Towards Optimal Statistical Watermarking. CoRR abs/2312.07930 (2023) - 2022
- [c6]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT 2022: 2730-2775 - [c5]Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao
Solving SDP Faster: A Robust IPM Framework and Efficient Implementation. FOCS 2022: 233-244 - [c4]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. ICLR 2022 - [i9]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR abs/2202.04634 (2022) - [i8]Baihe Huang, Zhao Song, Omri Weinstein, Hengjie Zhang, Ruizhe Zhang:
A Dynamic Fast Gaussian Transform. CoRR abs/2202.12329 (2022) - 2021
- [c3]Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang:
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. ICML 2021: 4423-4434 - [c2]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. NeurIPS 2021: 8968-8983 - [c1]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. NeurIPS 2021: 29101-29115 - [i7]Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao:
Solving Tall Dense SDPs in the Current Matrix Multiplication Time. CoRR abs/2101.08208 (2021) - [i6]Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang:
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis. CoRR abs/2105.05001 (2021) - [i5]Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi:
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. CoRR abs/2105.11066 (2021) - [i4]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. CoRR abs/2107.04518 (2021) - [i3]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. CoRR abs/2107.06466 (2021) - [i2]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. CoRR abs/2107.14702 (2021) - 2020
- [i1]Baihe Huang, Zhao Song, Runzhou Tao, Ruizhe Zhang, Danyang Zhuo:
InstaHide's Sample Complexity When Mixing Two Private Images. CoRR abs/2011.11877 (2020)
Coauthor Index
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