Tianxun Li — accepted AI-conference papers

2026

LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs

Zhinan Hou, Xingchen Li, Yankai Zhang, Tianxun Li, Keyou You

ICML 2026poster

Efficient branching policies are essential for accelerating Mixed Integer Linear Programming (MILP) solvers. Their design has long relied on hand-crafted heuristics, and now machine learning has emerged as a promising paradigm to automate this process. However, existing learning-based methods are of…

Cited by 0 Source ↗Scholar ↗

2025

Safety Meets Speed: Accelerated Neural MPC With Safety Guarantees and No Retraining

Kaikai Wang, Tianxun Li, Liang Xu, Qinglei Hu, Keyou You

RA-L 2025

While Model Predictive Control (MPC) enforces safety via constraints, its real-time execution can exceed embedded compute budgets. We propose a Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) framework that synergizes neural networks' fast computation with MPC's constraint-hand

Cited by 1 Source ↗Scholar ↗