1 citations · 2 across the 18 of their papers we have counts for
4 papers · 1 filter
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
Wei Xiong, Hanze Dong, Chenlu Ye +5
This paper studies the alignment process of generative models with Reinforcement Learning from Human Feedback (RLHF). We first identify the primary challenges of existing popular m…
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…
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Chenlu Ye, Rui Yang, Quanquan Gu +1
We investigate the problem of corruption robustness in offline reinforcement learning (RL) with general function approximation, where an adversary can corrupt each sample in the of…
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…