8 citations · 31 across the 13 of their papers we have counts for
6 papers · 1 filter
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…