Didier Chételat

According to our database, Didier Chételat authored at least 19 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

InnerThoughts: Disentangling Representations and Predictions in Large Language Models.

Didier Chételat, Joseph Cotnareanu, Rylee Thompson, Yingxue Zhang, Mark Coates

CoRR, January, 2025

2024

Hint Marginalization for Improved Reasoning in Large Language Models.

Soumyasundar Pal, Didier Chételat, Yingxue Zhang, Mark Coates

CoRR, 2024

The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation.

Reza Moravej, Saurabh Bodhe, Zhanguang Zhang, Didier Chételat, Dimitrios Tsaras, Yingxue Zhang, Hui-Ling Zhen, Jianye Hao, Mingxuan Yuan

CoRR, 2024

GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection.

Zhanguang Zhang, Didier Chételat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, Hui-Ling Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems.

Chendi Qian, Didier Chételat, Christopher Morris

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Continuous cutting plane algorithms in integer programming.

Didier Chételat, Andrea Lodi

Oper. Res. Lett., July, 2023

Combinatorial Optimization and Reasoning with Graph Neural Networks.

Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic

J. Mach. Learn. Res., 2023

Learning to Predict Security Constraints for Large-Scale Unit Commitment Problems.

Rafael Sterzinger, Jan Poland, Max B. Paulus, Didier Chételat

Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2023

2022

Lookback for Learning to Branch.

Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar

Trans. Mach. Learn. Res., 2022

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 M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao

CoRR, 2022

Learning to Branch with Tree MDPs.

Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning to Compare Nodes in Branch and Bound with Graph Neural Networks.

Abdel Ghani Labassi, Didier Chételat, Andrea Lodi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Ecole: A Library for Learning Inside MILP Solvers.

Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi

CoRR, 2021

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 M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

2020

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers.

Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi

CoRR, 2020

Change Point Detection by Cross-Entropy Maximization.

Aurélien Serre, Didier Chételat, Andrea Lodi

CoRR, 2020

2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks.

Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

On the domain of attraction of a Tracy-Widom law with applications to testing multiple largest roots.

Didier Chételat, Rajendran Narayanan, Martin T. Wells

J. Multivar. Anal., 2018

2016

Improved second order estimation in the singular multivariate normal model.

Didier Chételat, Martin T. Wells

J. Multivar. Anal., 2016