5 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
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…
cs.LG2021
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…
cs.LG2021★ 5 cited
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
Kefan Dong, Jiaqi Yang, Tengyu Ma
This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations. We propose to study convergence to approximate local maxima because we…