Portrait of Quentin Cappart

Quentin Cappart

Affiliate Member
Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Research Topics
Graph Neural Networks
Learning on Graphs
Reasoning
Website
Google Scholar
LinkedIn
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Biography

Quentin Cappart is an associate professor in the Department of Computer and Software Engineering at Polytechnique Montréal and an Affiliate member at Mila. He leads the CORAIL research group, which he co-founded with Louis-Martin Rousseau. Cappart obtained a BSc in engineering (2012), a MSc in computer engineering (2014), a MSc in management (2018) and a PhD (2017) at the Université catholique de Louvain (Belgium).

After his PhD, he joined Polytechnique Montréal and the International Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) as a postdoctoral fellow (2018–2020). During these two years, he was also a research intern at ElementAI. Cappart’s main research area is the integration of machine learning with search procedures for solving combinatorial problems.

Current Students

Léo Boisvert
PhD - Polytechnique Montréal
Principal supervisor :
Alexandre Drouin

Publications

An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems
Chao Yin
Quentin Cappart
Gilles Pesant
2023-12-31
Integration of AI and OR Techniques in Constraint Programming (published)
doi.org
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis
Quentin Cappart
Bistra Dilkina
Aaron M. Ferber
Louis-Martin Rousseau
2023-12-31
CP (published)
doi.org
arxiv.org
Learning Precedences for Scheduling Problems with Graph Neural Networks
Hélène Verhaeghe
Quentin Cappart
Gilles Pesant
Claude-Guy Quimper
2023-12-31
CP (published)
doi.org
Winning the 2023 CityLearn Challenge: A Community-Based Hierarchical Energy Systems Coordination Algorithm
Andoni I. Garmendia
Francesco Morri
Quentin Cappart
Hélène Le Cadre
. The effective management and control of building energy systems are crucial for reducing the energy consumption peak loads, CO 2 emissions… (see more), and ensuring the stability of the power grid, while maintaining optimal comfort levels within buildings. The difficulty to accommodate this trade-off is amplified by dynamic environmental conditions and the need for scalable solutions that can adapt across various building types and geographic locations. Acknowledging the importance of this problem, NeurIPS conference hosted since 2020 the CityLearn control challenge to foster the design of innovative solutions in building energy management. Participants were tasked with developing strategies that not only enhance energy efficiency but also prioritize sustainability and occupant comfort. This paper introduces the Community-based Hierarchical Energy Systems Co-ordination Algorithm ( CHESCA ), the winning approach of the 2023 edition. We rely on a hierarchical approach adaptable to an arbitrary number of buildings, first optimizing building-level metrics individually, and later refining these through a central community-level controller to improve grid-related metrics. Compared to the other high-ranked competitors, our approach demonstrated fast inference capabilities like learning-based methods, while offering a better interpretability and a superior generalization capabilities with minimal data requirements. This paper details our approach, supported by comprehensive experimental results and ablation studies.
2023-12-31
ECAI (published)
doi.org
Decision Diagrams in Space!
Isaac Rudich
Quentin Cappart
Manuel L'opez-Ib'anez
Michael Romer
Louis-Martin Rousseau
2023-12-02
ArXiv (preprint)
arxiv.org
arxiv.org
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vázquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-06
NeurIPS.cc/2023/Workshop/FMDM (published)
openreview.net
openreview.net
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Andoni I. Garmendia
Quentin Cappart
Josu Ceberio
Alexander Mendiburu
2023-08-18
Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (published)
doi.org
arxiv.org
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse
Simon Bowly
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 … (see 22 more)
Lara Scavuzzo
Giulia Zarpellon
Linxin Yang
Sha Lai
Akang Wang
Xiaodong Luo
Shuchang Zhou
Haohan Huang
Shengcheng Shao
Yuanming Zhu
Akang Wang
Mao Kun
Zixuan Cao
Yuanming Zhu
Zhewei Huang
Shuchang Zhou
C. Binbin
He Minggui
Hao Hao
Shuchang Zhou
Shuchang Zhou
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (see more)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.
2020-12-31
NeurIPS (Competition and Demos) (published)
doi.org
proceedings.mlr.press