1 citations · 2 across the 18 of their papers we have counts for
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Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
Chenlu Ye, Yujia Jin, Alekh Agarwal +1
Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range , and their regret scales polynomially with this reward range . Howeve…
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
Chenlu Ye, Jiafan He, Quanquan Gu +1
This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing stu…
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
Jianqing Fan, Zhaoran Wang, Zhuoran Yang +1
We study high-dimensional multi-armed contextual bandits with batched feedback where the steps of online interactions are divided into batches. In specific, each batch coll…
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin, Chen Liu, Chenlu Ye +3
Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational resources. To mitigate these cha…
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
Chenlu Ye, Wei Xiong, Quanquan Gu +1
Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead t…