Improved second order estimation in the singular multivariate normal model. - Didier Chételat; Martin T. Wells (J. Multivar. Anal., 2016)

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)

Exact Combinatorial Optimization with Graph Convolutional Neural Networks. - Maxime Gasse; Didier Chételat; Nicola Ferroni; Laurent Charlin; Andrea Lodi 001 (NeurIPS, 2019)

Change Point Detection by Cross-Entropy Maximization. - Aurélien Serre; Didier Chételat; Andrea Lodi 001 (CoRR, 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 001 (CoRR, 2020)

Combinatorial Optimization and Reasoning with Graph Neural Networks. - Quentin Cappart; Didier Chételat; Elias B. Khalil; Andrea Lodi 001; Christopher Morris 001; Petar Velickovic (IJCAI, 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; et al. (NeurIPS (Competition and Demos), 2021)

Ecole: A Library for Learning Inside MILP Solvers. - Antoine Prouvost; Justin Dumouchelle; Maxime Gasse; Didier Chételat; Andrea Lodi 001 (CoRR, 2021)

Learning to Compare Nodes in Branch and Bound with Graph Neural Networks. - Abdel Ghani Labassi; Didier Chételat; Andrea Lodi 001 (NeurIPS, 2022)

Learning to Branch with Tree MDPs. - Lara Scavuzzo; Feng Yang Chen; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Neil Yorke-Smith; Karen I. Aardal (NeurIPS, 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 001; et al. (CoRR, 2022)

Lookback for Learning to Branch. - Prateek Gupta; Elias Boutros Khalil; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Yoshua Bengio; M. Pawan Kumar (Trans. Mach. Learn. Res., 2022)

Learning to Predict Security Constraints for Large-Scale Unit Commitment Problems. - Rafael Sterzinger; Jan Poland; Max B. Paulus; Didier Chételat (ISGT EUROPE, 2023)

Continuous cutting plane algorithms in integer programming. - Didier Chételat; Andrea Lodi 001 (Oper. Res. Lett., 2023)

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems. - Chendi Qian; Didier Chételat; Christopher Morris 001 (AISTATS, 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 001; Jianye Hao; Mark Coates; Mingxuan Yuan (KDD, 2024)

Hint Marginalization for Improved Reasoning in Large Language Models. - Soumyasundar Pal; Didier Chételat; Yingxue Zhang 001; Mark Coates (CoRR, 2024)

InnerThoughts: Disentangling Representations and Predictions in Large Language Models. - Didier Chételat; Joseph Cotnareanu; Rylee Thompson; Yingxue Zhang 001; Mark Coates (AISTATS, 2025)

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 001; Hui-Ling Zhen; Jianye Hao; Mingxuan Yuan (IJCAI, 2025)