6 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…
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…
Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy
Young Hyun Cho, Jordan Awan
Privacy protection and uncertainty quantification are increasingly important in data-driven decision making. Conformal prediction provides finite-sample marginal coverage, but exis…
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…
Formal Privacy Guarantees with Invariant Statistics
Young Hyun Cho, Jordan Awan
Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invarian…