From the 1 of 20 linked papers with an AI index.
3 citations · 3 across the 5 of their papers we have counts for
7 papers · 1 filter
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Bingheng Li, Junyang Cai, Yupeng Zhang +3
The paper presents FunL2O, a framework that uses large language models to automatically generate feature functions for learning-to-optimize systems, showing improved performance ov…
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján DrgoÅa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
Weimin Huang, Ryan Piansky, Bistra Dilkina +1
To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to man…
Application-Driven Innovation in Machine Learning
David Rolnick, Alan Aspuru-Guzik, Sara Beery +8
In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning prolifer…
LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoning
Weizhe Chen, Sven Koenig, Bistra Dilkina
Since the release of Deepseek-R1, reinforcement learning with verifiable rewards (RLVR) has become a central approach for training large language models (LLMs) on reasoning tasks.…
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
Weimin Huang, Taoan Huang, Aaron M Ferber +1
Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used machine learning to ac…