InstaHide's Sample Complexity When Mixing Two Private Images. - Baihe Huang; Zhao Song 002; Runzhou Tao; Ruizhe Zhang 001; Danyang Zhuo (CoRR, 2020)

FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. - Baihe Huang; Xiaoxiao Li; Zhao Song 002; Xin Yang 017 (ICML, 2021)

Going Beyond Linear RL: Sample Efficient Neural Function Approximation. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

Solving Tall Dense SDPs in the Current Matrix Multiplication Time. - Baihe Huang; Shunhua Jiang; Zhao Song 002; Runzhou Tao (CoRR, 2021)

FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis. - Baihe Huang; Xiaoxiao Li; Zhao Song 002; Xin Yang 017 (CoRR, 2021)

Offline Reinforcement Learning with Realizability and Single-policy Concentrability. - Wenhao Zhan; Baihe Huang; Audrey Huang; Nan Jiang 008; Jason D. Lee (COLT, 2022)

Solving SDP Faster: A Robust IPM Framework and Efficient Implementation. - Baihe Huang; Shunhua Jiang; Zhao Song 002; Runzhou Tao; Ruizhe Zhang 001 (FOCS, 2022)

Towards General Function Approximation in Zero-Sum Markov Games. - Baihe Huang; Jason D. Lee; Zhaoran Wang 001; Zhuoran Yang (ICLR, 2022)

A Dynamic Fast Gaussian Transform. - Baihe Huang; Zhao Song 002; Omri Weinstein; Hengjie Zhang; Ruizhe Zhang 001 (CoRR, 2022)

Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. - Qian Yu; Yining Wang; Baihe Huang; Qi Lei; Jason D. Lee (AISTATS, 2023)

Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. - Qian Yu; Yining Wang; Baihe Huang; Qi Lei; Jason D. Lee (NeurIPS, 2023)

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. - Baihe Huang; Sai Praneeth Karimireddy; Michael I. Jordan (CoRR, 2023)

Towards Optimal Statistical Watermarking. - Baihe Huang; Banghua Zhu; Hanlin Zhu; Jason D. Lee; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. - Wenhao Zhan; Shicong Cen; Baihe Huang; Yuxin Chen 002; Jason D. Lee; Yuejie Chi (SIAM J. Optim., 2023)

Enhancing Language Model Alignment: A Confidence-Based Approach to Label Smoothing. - Baihe Huang; Hiteshi Sharma; Yi Mao (EMNLP, 2024)

On Representation Complexity of Model-based and Model-free Reinforcement Learning. - Hanlin Zhu; Baihe Huang; Stuart Russell 001 (ICLR, 2024)

Data Acquisition via Experimental Design for Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (NeurIPS, 2024)

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. - Qian Yu; Yining Wang; Baihe Huang; Qi Lei; Jason D. Lee (NeurIPS, 2024)

Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. - Hanlin Zhu; Baihe Huang; Shaolun Zhang; Michael I. Jordan; Jiantao Jiao; Yuandong Tian; Stuart J. Russell (NeurIPS, 2024)

Data Acquisition via Experimental Design for Decentralized Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (CoRR, 2024)

Sounding that Object: Interactive Object-Aware Image to Audio Generation. - Tingle Li; Baihe Huang; Xiaobin Zhuang; Dongya Jia; Jiawei Chen; Yuping Wang 005; Zhuo Chen 006; Gopala Anumanchipalli; Yuxuan Wang 002 (CoRR, 2025)

Sample Complexity and Representation Ability of Test-time Scaling Paradigms. - Baihe Huang; Shanda Li; Tianhao Wu 002; Yiming Yang; Ameet Talwalkar; Kannan Ramchandran; Michael I. Jordan; Jiantao Jiao (CoRR, 2025)