26 citations · 53 across the 5 of their papers we have counts for
7 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…
Ecole: A Library for Learning Inside MILP Solvers
Antoine Prouvost, Justin Dumouchelle, Maxime Gasse +2
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization…
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo +3
We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinator…
Change Point Detection by Cross-Entropy Maximization
Aurélien Serre, Didier Chételat, Andrea Lodi
Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs. We extend this framework by proposing to minimize a sum of dis…
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni +2
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound…
The middle-scale asymptotics of Wishart matrices
Didier Chételat, Martin T. Wells
We study the behavior of a real -dimensional Wishart random matrix with degrees of freedom when but . We establish the existence of…