75 citations · 202 across the 39 of their papers we have counts for
7 papers · 1 filter
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback
Seong Jin Lee, Will Wei Sun, Yufeng Liu
Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback,…
Privacy-Preserving Dynamic Assortment Selection
Young Hyun Cho, Will Wei Sun
With the growing demand for personalized assortment recommendations, concerns over data privacy have intensified, highlighting the urgent need for effective privacy-preserving stra…
Dual Active Learning for Reinforcement Learning from Human Feedback
Pangpang Liu, Chengchun Shi, Will Wei Sun
Aligning large language models (LLMs) with human preferences is critical to recent advances in generative artificial intelligence. Reinforcement learning from human feedback (RLHF)…
Active Learning for Fair and Stable Online Allocations
Riddhiman Bhattacharya, Thanh Nguyen, Will Wei Sun +1
We explore an active learning approach for dynamic fair resource allocation problems. Unlike previous work that assumes full feedback from all agents on their allocations, we consi…
Low-Rank Online Dynamic Assortment with Dual Contextual Information
Seong Jin Lee, Will Wei Sun, Yufeng Liu
As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful con…
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
Danyang Wang, Chengchun Shi, Shikai Luo +1
In real-world scenarios, datasets collected from randomized experiments are often constrained by size, due to limitations in time and budget. As a result, leveraging large observat…
Rate-Optimal Rank Aggregation with Private Pairwise Rankings
Shirong Xu, Will Wei Sun, Guang Cheng
In various real-world scenarios, such as recommender systems and political surveys, pairwise rankings are commonly collected and utilized for rank aggregation to derive an overall…