75 citations · 202 across the 39 of their papers we have counts for
41 papers
Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations
Young Hyun Cho, Franz Stoll, Will Wei Sun +2
Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change. Many operational systems also have hierarchical structu…
When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems
Young Hyun Cho, Will Wei Sun
LLM-enabled AI workflows increasingly produce outputs through iterative generate-evaluate-revise loops. Each iteration can improve the candidate, but it also creates a release deci…
Low Rank for Rank: Uncertainty-Aware Task-Specific LLM Ranking under Sparse Pairwise Comparisons
Jiachun Li, David Simchi-Levi, Will Wei Sun
Pairwise human-preference platforms such as Chatbot Arena have become central to large language model (LLM) evaluation, yet reliable task-specific ranking remains challenging. Glob…
Policy-Aware Design of Large-Scale Factorial Experiments
Xin Wen, Xi Chen, Will Wei Sun +1
Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of interface elements, flows, message…
Reinforcement Learning from Human Feedback: A Statistical Perspective
Pangpang Liu, Chengchun Shi, Will Wei Sun
Reinforcement learning from human feedback (RLHF) has emerged as a central framework for aligning large language models (LLMs) with human preferences. Despite its practical success…
LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
Jiachun Li, David Simchi-Levi, Will Wei Sun
Large language model (LLM) evaluation platforms increasingly rely on pairwise human judgments. These data are noisy, sparse, and non-uniform, yet leaderboards are reported with lim…
Privacy-Preserving Reinforcement Learning from Human Feedback via Decoupled Reward Modeling
Young Hyun Cho, Will Wei Sun
Preference-based fine-tuning has become an important component in training large language models, and the data used at this stage may contain sensitive user information. A central…
Do More Predictions Improve Statistical Inference? Filtered Prediction-Powered Inference
Shirong Xu, Will Wei Sun
Recent advances in artificial intelligence have enabled the generation of large-scale, low-cost predictions with increasingly high fidelity. As a result, the primary challenge in s…
PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization
Ruicheng Ao, Hongyu Chen, Haoyang Liu +2
We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through contro…
Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback
Pangpang Liu, Junwei Lu, Will Wei Sun
We study estimation and statistical inference for reward models used in aligning large language models (LLMs). A key component of LLM alignment is reinforcement learning from human…
Generalized Tensor Completion with Non-Random Missingness
Maoyu Zhang, Biao Cai, Will Wei Sun +1
Tensor completion plays a crucial role in applications such as recommender systems and medical imaging, where data are often highly incomplete. While extensive prior work has addre…
Fairness-aware Contextual Dynamic Pricing with Strategic Buyers
Pangpang Liu, Will Wei Sun
Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies…
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…
Online Tensor Inference
Xin Wen, Will Wei Sun, Yichen Zhang
Contemporary applications, such as recommendation systems and mobile health monitoring, require real-time processing and analysis of sequentially arriving high-dimensional tensor d…
Contextual Dynamic Pricing with Strategic Buyers
Pangpang Liu, Zhuoran Yang, Zhaoran Wang +1
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this proces…
Utility Theory of Synthetic Data Generation
Shirong Xu, Will Wei Sun, Guang Cheng
Synthetic data algorithms are widely employed in industries to generate artificial data for downstream learning tasks. While existing research primarily focuses on empirically eval…
Ranking Differential Privacy
Shirong Xu, Will Wei Sun, Guang Cheng
Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, exis…
Online Statistical Inference in Decision-Making with Matrix Context
Qiyu Han, Will Wei Sun, Yichen Zhang
The study of online decision-making problems that leverage contextual information has drawn notable attention due to their significant applications in fields ranging from healthcar…