A Homological Approach to Belief Propagation and Bethe Approximations. - Olivier Peltre (GSI, 2019)

Introduction to Geometric Learning in Python with Geomstats. - Nina Miolane; Nicolas Guigui; Hadi Zaatiti; Christian Shewmake; Hatem Hajri; Daniel Brooks; Alice Le Brigant; Johan Mathe; Benjamin Hou; Yann Thanwerdas; Stefan Heyder; Olivier Peltre; et al. (SciPy, 2020)

Geomstats: A Python Package for Riemannian Geometry in Machine Learning. - Nina Miolane; Alice Le Brigant; Johan Mathe; Benjamin Hou; Nicolas Guigui; Yann Thanwerdas; Stefan Heyder; Olivier Peltre; Niklas Koep; Hadi Zaatiti; Hatem Hajri; Yann Cabanes; et al. (CoRR, 2020)

Belief Propagation as Diffusion. - Olivier Peltre (GSI, 2021)

Local Max-Entropy and Free Energy Principles Solved by Belief Propagation. - Olivier Peltre (CoRR, 2022)

Equivariant Message Passing Neural Network for Crystal Material Discovery. - Astrid Klipfel; Zied Bouraoui; Olivier Peltre; Yaël Frégier; Najwa Harrati; Adlane Sayede (AAAI, 2023)

Local Max-Entropy and Free Energy Principles, Belief Diffusions and their Singularities. - Olivier Peltre (CoRR, 2023)

Universally applicable and tunable graph-based coarse-graining for Machine learning force fields. - Christoph Brunken; Sebastien Boyer; Mustafa Omar; Martin Maarand; Olivier Peltre; Solal Attias; Bakary N'tji Diallo; Anastasia Markina; Olaf Othersen; Oliver Bent (CoRR, 2025)