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Mingxuan Ye

3 accepted papers

2026

Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification

ICML 2026poster

Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language models (LLMs) to automate this modeling process. However, existing works often struggle to verify the correctness of the …

Cited by 0SourceScholar
2025

Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph

NeurIPS 2025poster

Cutting planes (cuts) are essential for solving mixed-integer linear programming (MILP) problems, as they tighten the feasible solution space and accelerate the solving process. Modern MILP solvers offer diverse cutting plane separators to generate cuts, enabling users to leverage their potential co…

Cited by 0SourceScholar
2023

State Sequences Prediction via Fourier Transform for Representation Learning

NeurIPS 2023spotlight

While deep reinforcement learning (RL) has been demonstrated effective in solving complex control tasks, sample efficiency remains a key challenge due to the large amounts of data required for remarkable performance. Existing research explores the application of representation learning for data-effi…