9 citations · 21 across the 9 of their papers we have counts for
10 papers
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…
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis, Quentin Cappart, Bistra Dilkina +2
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality…
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…
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Taoan Huang, Aaron Ferber, Yuandong Tian +2
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhoo…
Local Branching Relaxation Heuristics for Integer Linear Programs
Taoan Huang, Aaron Ferber, Yuandong Tian +2
Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iterat…
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…
Learning Pseudo-Backdoors for Mixed Integer Programs
Aaron Ferber, Jialin Song, Bistra Dilkina +1
We propose a machine learning approach for quickly solving Mixed Integer Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for b…
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Umang Gupta, Aaron M Ferber, Bistra Dilkina +1
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the da…
MIPaaL: Mixed Integer Program as a Layer
Aaron Ferber, Bryan Wilder, Bistra Dilkina +1
Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between…