9 citations · 29 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
GenCO: Generating Diverse Designs with Combinatorial Constraints
Aaron Ferber, Arman Zharmagambetov, Taoan Huang +2
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images. However, many design settings arising in industrial design, m…
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Arman Zharmagambetov, Brandon Amos, Aaron Ferber +3
Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perfor…
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Aaron Ferber, Taoan Huang, Daochen Zha +4
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to the…