19 citations · 43 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…