75 citations · 202 across the 39 of their papers we have counts for
9 papers · 1 filter
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…