11 citations · 35 across the 15 of their papers we have counts for
28 papers
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
Stephan Wäldchen, Felix Huber, Sebastian Pokutta
One of the goals of Explainable AI (XAI) is to determine which input components were relevant for a classifier decision. This is commonly know as saliency attribution. Characterist…
Efficient Online-Bandit Strategies for Minimax Learning Problems
Christophe Roux, Elias Wirth, Sebastian Pokutta +1
Several learning problems involve solving min-max problems, e.g., empirical distributional robust learning or learning with non-standard aggregated losses. More specifically, these…
An Algorithm-Independent Measure of Progress for Linear Constraint Propagation
Boro Sofranac, Ambros Gleixner, Sebastian Pokutta
Propagation of linear constraints has become a crucial sub-routine in modern Mixed-Integer Programming (MIP) solvers. In practice, iterative algorithms with tolerance-based stoppin…
FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients
Mathieu Besançon, Alejandro Carderera, Sebastian Pokutta
We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is d…
Learning to Schedule Heuristics in Branch-and-Bound
Antonia Chmiela, Elias B. Khalil, Ambros Gleixner +2
Primal heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP). While solvers are guaranteed to find optimal solutions given sufficient time, real-world…