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Competition
Machine Learning for Combinatorial Optimization (ML4CO)
Christopher Morris · Maxime Gasse
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Pseudo workshop for Competition Track and Gather Town support
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MDO: MDO's Methods for NeurIPS 2021 Machine Learning for Combinatorial Optimization Competition
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CUHKSZ_ATD: Efficient Primal Heuristics for Mixed-Integer Linear Programs
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KAIST_OSI: Strong Regularization is All You Need
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ethz-scuba: Finding good primal solutions for integer programs with a tuned solver
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UNIST-LIM-Lab: Confidence Is All You Need
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MixedInspiredLamePuns: Instance-wise Algorithm Configuration with Graph Neural Networks
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QQY: YORDLE: An Efficient Imitation Learning for Branch and Bound
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Author Information
Christopher Morris (Mila, McGill University)
Maxime Gasse (Polytechnique Montréal)
I am a machine learning researcher within the Data Science for Real-Time Decision Making Canada Excellence Research Chair (CERC), and also part of the MILA research institute on artificial intelligence in Montréal, Canada. The question that motivates my research is: can machines think? My broad research interests include: - probabilistic graphical models and their theoretical properties (my PhD Thesis) - structured prediction, in particular multi-label classification - combinatorial optimization using machine learning (see our Ecole library) - causality, specifically in the context of reinforcement learning
More from the Same Authors
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2022 : Using Confounded Data in Offline RL »
Maxime Gasse · Damien GRASSET · Guillaume Gaudron · Pierre-Yves Oudeyer -
2022 Poster: Learning to Branch with Tree MDPs »
Lara Scavuzzo · Feng Chen · Didier Chetelat · Maxime Gasse · Andrea Lodi · Neil Yorke-Smith · Karen Aardal -
2022 Poster: Ordered Subgraph Aggregation Networks »
Chendi Qian · Gaurav Rattan · Floris Geerts · Mathias Niepert · Christopher Morris -
2021 : Machine Learning for Combinatorial Optimization + Q&A »
Maxime Gasse · Simon Bowly · Chris Cameron · Quentin Cappart · Jonas Charfreitag · Laurent Charlin · Shipra Agrawal · Didier Chetelat · Justin Dumouchelle · Ambros Gleixner · Aleksandr Kazachkov · Elias Khalil · Pawel Lichocki · Andrea Lodi · Miles Lubin · Christopher Morris · Dimitri Papageorgiou · Augustin Parjadis · Sebastian Pokutta · Antoine Prouvost · Yuandong Tian · Lara Scavuzzo · Giulia Zarpellon -
2020 Poster: Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings »
Christopher Morris · Gaurav Rattan · Petra Mutzel -
2020 Poster: Hybrid Models for Learning to Branch »
Prateek Gupta · Maxime Gasse · Elias Khalil · Pawan K Mudigonda · Andrea Lodi · Yoshua Bengio