30 citations · 109 across the 31 of their papers we have counts for
4 papers · 1 filter
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
Jiafan He, Heyang Zhao, Dongruo Zhou +1
We study reinforcement learning (RL) with linear function approximation. For episodic time-inhomogeneous linear Markov decision processes (linear MDPs) whose transition probability…
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Jiafan He, Tianhao Wang, Yifei Min +1
We study federated contextual linear bandits, where agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We co…
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Jiafan He, Dongruo Zhou, Tong Zhang +1
We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e.…
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
Heyang Zhao, Dongruo Zhou, Jiafan He +1
We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additiv…