Didier Chételat

I am a senior researcher at Huawei’s Noah’s Ark Lab in Montreal, Canada, in the graph deep learning team. Previously, I was researcher at the Canada Excellence Research Chair in Data Science for Real Time Decision Making. I obtained my Ph.D. in Statistics at Cornell University in 2015 under the supervision of Martin T. Wells.
My current research interests center around graph deep learning, combinatorial optimization, and reasoning by intelligent agents.

Software

Ecole    [code] [documentation] A Python library for Gym-like integration of machine learning in combinatorial optimization solvers.

Publications

• Continuous cutting plane algorithms in integer programming    [code]
  Operations Research Letters. To appear. Didier Chételat, Andrea Lodi.
• Combinatorial optimization and reasoning with graph neural networks (Extended version)
  Journal of Machine Learning Research 24, 1–61. Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković.

• Learning to branch with tree MDPs    [code]
  Advances in Neural Information Processing Systems 35, 18514–18526. Lara Scavuzzo, Feng Y. Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen Aardal.
• Learning to compare nodes in branch and bound with graph neural networks    [code]
  Advances in Neural Information Processing Systems 35, 32000–32010. Abdel G. Labassi, Didier Chételat, Andrea Lodi.
• Lookback for learning to branch
  Transactions on Machine Learning Research, November 2022. Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi and P. M. Kumar.

• Combinatorial optimization and reasoning with graph neural networks
  International Joint Conference on Artificial Intelligence 30, 4348-4355. Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković.

• Ecole: A Gym-like library for machine learning in combinatorial optimization solvers    [code]
  Learning Meets Combinatorial Algorithms Neurips Workshop. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi.

• Exact combinatorial optimization with graph convolutional neural networks    [code]
  Advances in Neural Information Processing Systems 32, 15554–15566. Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi.
• The middle-scale asymptotics of Wishart matrices
  Annals of Statistics 47, 2639-2670. Didier Chételat, Martin T. Wells.

• On the domain of attraction of a Tracy–Widom law with applications to testing multiple largest roots
  Journal of Multivariate Analysis 165, 132-142. Didier Chételat, Rajendran Narayanan, Martin T. Wells.

• Optimal two-step prediction in regression
  Electronic Journal of Statistics 11, 2519-2546. Didier Chételat, Joseph Salmon, Johannes Lederer.

• Improved second order estimation in the singular multivariate normal model
  Journal of Multivariate Analysis 147, 1-19. Didier Chételat, Martin T. Wells.

• Improved multivariate normal mean estimation with unknown covariance when p is greater than n
  Annals of Statistics 40, 3137-3160. Didier Chételat, Martin T. Wells.