9 citations · 29 across the 13 of their papers we have counts for
13 papers
Multi-task Representation Learning for Mixed Integer Linear Programming
Junyang Cai, Taoan Huang, Bistra Dilkina
Mixed Integer Linear Programs (MILPs) are highly flexible and powerful tools for modeling and solving complex real-world combinatorial optimization problems. Recently, machine lear…
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 Backdoors for Mixed Integer Linear Programs with Contrastive Learning
Junyang Cai, Taoan Huang, Bistra Dilkina
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method. Prior work has shown the existence of MILP…
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Thomy Phan, Taoan Huang, Bistra Dilkina +1
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Ne…
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…
The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems
Sumedh Pendurkar, Taoan Huang, Sven Koenig +1
The A* algorithm is commonly used to solve NP-hard combinatorial optimization problems. When provided with a completely informed heuristic function, A* solves many NP-hard minimum-…
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
Taoan Huang, Bistra Dilkina, Sven Koenig
Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting…
Green Security Game with Community Engagement
Taoan Huang, Weiran Shen, David Zeng +3
While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work…
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Taoan Huang, Bohui Fang, Xiaohui Bei +1
Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, lead…