Statistical Comparison of Algorithm Performance Through Instance Selection. - Théo Matricon; Marie Anastacio; Nathanaël Fijalkow; Laurent Simon 001; Holger H. Hoos (CP, 2021)

Scaling Neural Program Synthesis with Distribution-Based Search. - Nathanaël Fijalkow; Guillaume Lagarde; Théo Matricon; Kevin Ellis; Pierre Ohlmann; Akarsh Nayan Potta (AAAI, 2022)

Challenges of Acquiring Compositional Inductive Biases via Meta-Learning. - Marie Anastacio; Théo Matricon; Holger H. Hoos (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

DeepSynth: Scaling Neural Program Synthesis with Distribution-based Search. - Théo Matricon; Nathanaël Fijalkow; Guillaume Lagarde; Kevin Ellis (J. Open Source Softw., 2022)

WikiCoder: Learning to Write Knowledge-Powered Code. - Théo Matricon; Nathanaël Fijalkow; Gaëtan Margueritte (SPIN, 2023)

Theoretical foundations for programmatic reinforcement learning. - Guruprerana Shabadi; Nathanaël Fijalkow; Théo Matricon (CoRR, 2024)

EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis. - Théo Matricon; Nathanaël Fijalkow; Guillaume Lagarde (CoRR, 2024)

Domain agnostic techniques for scaling up program synthesis. (Techniques agnostiques au domaine pour l'accélération de la synthèse de programmes). - Théo Matricon (2024)

Eco Search: A No-delay Best-First Search Algorithm for Program Synthesis. - Théo Matricon; Nathanaël Fijalkow; Guillaume Lagarde (AAAI, 2025)

Re-evaluating metamorphic testing of chess engines: A replication study. - Axel Martin; Djamel Eddine Khelladi; Théo Matricon; Mathieu Acher (Inf. Softw. Technol., 2025)

Prompting for Performance: Exploring LLMs for Configuring Software. - Helge Spieker; Théo Matricon; Nassim Belmecheri; Jørn Eirik Betten; Gauthier Le Bartz Lyan; Heraldo Borges; Quentin Mazouni; Dennis Gross; Arnaud Gotlieb; Mathieu Acher (CoRR, 2025)