Data Driven Combinatorial Optimization

Methodological contributions at the crossroad of OR and ML for data driven combinatorial optimization problems

The aim of this workshop is to bring together experts from the machine learning and operations research communities to discuss methodologies for data driven combinatorial optimization problems.

October 5th, 2023

Room B211, Coriolis building, Ecole des Ponts

Registration is free but mandatory. Only 30 participants will be accepted.

Program

9:00 Coffee

9:25 Welcome address

9:30-11:00 Talks

  • Tobias Sutter (University of Konstanz) Data-driven distributionally robust optimization: Classification of ambiguity sets. Slides.
  • Jérôme Malick (University Grenoble Alps) Wasserstein distributionally robust optimization in action. Slides.

11:00-11:30 Coffee

11:30-13:00 Talks

  • Maximilian Schiffer (Technische Universität München) and Axel Parmentier (École des Ponts). Joint talk on Combinatorial Optimization Enriched Machine Learning to Solve Dynamic Vehicle Routing Problems.
  • Louis Bouvier (École des Ponts). InferOpt: A julia package for combinatorial optimization layers in neural networks.
  • Tobias Enders (Technische Universität München)

13:00-14:30 Lunch

14:30-16:00 Talks

  • Mathieu Blondel (Google) Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective.
  • Dario Paccagnan (Imperial College) The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance.

16:00-16:30 Coffee

16:30-17:30 Questions and Open Problems

Organizers

Maximilian Schiffer and Axel Parmentier

Sponsors

DAAO axis of GDR ROD

ROADEF