19 citations · 43 across the 7 of their papers we have counts for
8 papers
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Justin Dumouchelle, Shunyu Yao, Beste Basciftci +1
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into…
Neur2BiLO: Neural Bilevel Optimization
Justin Dumouchelle, Esther Julien, Jannis Kurtz +1
Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reac…
Deep Learning for Two-Stage Robust Integer Optimization
Justin Dumouchelle, Esther Julien, Jannis Kurtz +1
Robust optimization is an established framework for modeling optimization problems with uncertain parameters. While static robust optimization is often criticized for being too con…
Neur2SP: Neural Two-Stage Stochastic Programming
Justin Dumouchelle, Rahul Patel, Elias B. Khalil +1
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used cl…
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…
Reinforcement Learning for Freight Booking Control Problems
Justin Dumouchelle, Emma Frejinger, Andrea Lodi
Booking control problems are sequential decision-making problems that occur in the domain of revenue management. More precisely, freight booking control focuses on the problem of d…
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…