Theoretical foundations for programmatic reinforcement learning

Guruprerana Shabadi, Nathanaël Fijalkow, Théo Matricon
Published in under review



WikiCoder: Learning to Write Knowledge-Powered Code

Théo Matricon, Nathanaël Fijalkow, Gaëtan Margueritte
Published in SPIN (ETAPS)

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Software: DeepSynth

Théo Matricon, Nathanaël Fijalkow, Guillaume Lagarde, Kevin Ellis
Published in JOSS

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Scaling Neural Program Synthesis with Distribution-based Search

Nathanaël Fijalkow, Guillaume Lagarde, Théo Matricon, Kevin Ellis, Pierre Ohlmann, Akarsh Potta
Published in AAAI 2022 (Oral, ~5% of submissions accepted for oral, 15% acceptance rate, 9k+ submissions)

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Statistical Comparison of Algorithm Performance Through Instance Selection

Théo Matricon, Marie Anastacio, Nathanaël Fijalkow, Laurent Simon, Holger H. Hoos
Published in CP 2021 (30% acceptance rate)

DOI Code Talk Slides