Lunch Seminars


  • January 10, 17 and 24, Recent Advances in Integrating Machine Learning and Combinatorial Optimization – Tutorial at AAAI-21
      • Tutorial webpage with slides
      • Part 1: Introduction to combinatorial optimization & tutorial overview
        Part 2: The pure ML approach: predicting feasible solutions
        Part 3: The hybrid approach: improving exact solvers with ML
        Part 4: Machine learning for MIP solving: challenges & literature
        Part 5: Ecole: A python framework for learning in exact MIP solvers
        Part 6: Decision-focused Learning
        Part 7: Concluding remarks
      • This tutorial will provide an overview of the recent impact machine learning is having on combinatorial optimization, particularly under the Mixed Integer Programming (MIP) framework. Topics covered will include ML and reinforcement learning for predicting feasible solutions, improving exact solvers with ML, a software framework for learning in exact MIP solvers, and the emerging paradigm of decision-focused learning.
      • The tutorial targets both junior and senior researchers in two prominent areas of interest to the AAAI community: (1) Machine learning researchers looking for a challenging application domain, namely combinatorial optimization; (2) Optimization practitioners and researchers who may benefit from learning about recent advances in ML methods for improving combinatorial optimization algorithms.
      • Presented by: Elias B. Khalil (University of Toronto), Andrea Lodi (Polytechnique Montréal), Bistra Dilkina (University of Southern California), Didier Chételat (Polytechnique Montréal), Maxime Gasse (Polytechnique Montréal), Antoine Prouvost (Polytechnique Montréal), Giulia Zarpellon (Polytechnique Montréal) and Laurent Charlin (HEC Montréal)