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Antoine Prouvost

Alumni

Publications

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse
Quentin Cappart
Jonas Charfreitag
Laurent Charlin
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr M. Kazachkov
Elias Khalil
Pawel Lichocki
Andrea Lodi
Miles Lubin
Chris J. Maddison
Christopher Morris
Dimitri J. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost
Lara Scavuzzo … (voir 21 de plus)
Giulia Zarpellon
Linxin Yang
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Shengcheng Shao
Yuanming Zhu
Dong Zhang
Tao Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
Chen Binbin
He Minggui
Hao Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
2022-07-19
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track (publié)
doi.org
proceedings.mlr.press
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
Yoshua Bengio
Andrea Lodi
Antoine Prouvost
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning t… (voir plus)o solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
2020-12-31
Eur. J. Oper. Res. (publié)
doi.org
arxiv.org