3 | Tinghan (Joe) Ye

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

Wed, 24 Jun 2026 00:00:00 +0000

Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback

Sun, 31 May 2026 00:00:00 +0000

This preprint develops an on-policy decision-focused learning method for sequential contextual linear optimization with partial feedback.

The approach learns a stochastic predict-then-optimize policy and updates it with a hybrid gradient estimator combining score-function and decision-focused plug-in components. Experiments cover top-k selection, shortest path, combinatorial pricing, and energy scheduling.

Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches

Wed, 27 May 2026 00:00:00 +0000

This preprint introduces an agentic re-optimization framework in which an LLM translates user requests into structured optimization model patches, selects re-optimization tools, and solves updated instances.

The experiments span supply-chain re-optimization and university exam scheduling, showing efficiency gains from primal-based and solver-aware methods while preserving traceable model changes.

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

Thu, 21 May 2026 00:00:00 +0000

This preprint studies path-dependent uncertainty in agentic text-simulation systems. HawkesLLM separates temporal influence modeling from text generation: a multivariate Hawkes process selects compact prompt memory, and a language model writes each new event from that memory.