| 2025 | AAAI | Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles. | Youssouf Emine, Alexandre Forel, Idriss Malek, Thibaut Vidal |
| 2025 | AIES | Fairness and Sparsity Within Rashomon Sets: Enumeration-Free Exploration and Characterization. | Lucas Langlade, Julien Ferry, Gabriel Laberge, Thibaut Vidal |
| 2024 | CPAIOR | Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles. | Alexandre Forel, Axel Parmentier, Thibaut Vidal |
| 2024 | ICML | Trained Random Forests Completely Reveal your Dataset. | Julien Ferry, Ricardo Fukasawa, Timothe Pascal, Thibaut Vidal |
| 2024 | ICML | CF-OPT: Counterfactual Explanations for Structured Prediction. | Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut Vidal |
| 2023 | AAAI | Optimal Decision Diagrams for Classification. | Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra, Thibaut Vidal |
| 2023 | CPAIOR | Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill? | talo Santana, Andrea Lodi, Thibaut Vidal |
| 2023 | ICML | Explainable Data-Driven Optimization: From Context to Decision and Back Again. | Alexandre Forel, Axel Parmentier, Thibaut Vidal |
| 2021 | ICML | Optimal Counterfactual Explanations in Tree Ensembles. | Axel Parmentier, Thibaut Vidal |
| 2020 | ICML | Born-Again Tree Ensembles. | Thibaut Vidal, Maximilian Schiffer |
| 2020 | ICPR | Assortative-Constrained Stochastic Block Models. | Daniel Gribel, Thibaut Vidal, Michel Gendreau |