8 citations · 31 across the 13 of their papers we have counts for
13 papers
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
Qian Yu, Yining Wang, Baihe Huang +2
Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning. In this work, we consider the problem of optim…
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performanc…
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
Qian Yu, Yining Wang, Baihe Huang +2
In stochastic zeroth-order optimization, a problem of practical relevance is understanding how to fully exploit the local geometry of the underlying objective function. We consider…
A Faster Quantum Algorithm for Semidefinite Programming via Robust IPM Framework
Baihe Huang, Shunhua Jiang, Zhao Song +2
This paper studies a fundamental problem in convex optimization, which is to solve semidefinite programming (SDP) with high accuracy. This paper follows from the existing robust SD…
A Dynamic Low-Rank Fast Gaussian Transform
Baihe Huang, Zhao Song, Omri Weinstein +3
The \emph{Fast Gaussian Transform} (FGT) enables subquadratic-time multiplication of an Gaussian kernel matrix with…
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Wenhao Zhan, Baihe Huang, Audrey Huang +2
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e.g., Bellman-completeness) and the data coverag…
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Baihe Huang, Kaixuan Huang, Sham M. Kakade +4
Bandit problems with linear or concave reward have been extensively studied, but relatively few works have studied bandits with non-concave reward. This work considers a large fami…
Towards General Function Approximation in Zero-Sum Markov Games
Baihe Huang, Jason D. Lee, Zhaoran Wang +1
This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is…
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham M. Kakade +4
Deep Reinforcement Learning (RL) powered by neural net approximation of the Q function has had enormous empirical success. While the theory of RL has traditionally focused on linea…
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Baihe Huang, Xiaoxiao Li, Zhao Song +1
Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have b…
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Wenhao Zhan, Shicong Cen, Baihe Huang +3
Policy optimization, which finds the desired policy by maximizing value functions via optimization techniques, lies at the heart of reinforcement learning (RL). In addition to valu…
Solving SDP Faster: A Robust IPM Framework and Efficient Implementation
Baihe Huang, Shunhua Jiang, Zhao Song +2
This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hy…
InstaHide's Sample Complexity When Mixing Two Private Images
Baihe Huang, Zhao Song, Runzhou Tao +3
Training neural networks usually require large numbers of sensitive training data, and how to protect the privacy of training data has thus become a critical topic in deep learning…