Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport. - Théo Lacombe; Marco Cuturi; Steve Oudot (NeurIPS, 2018)

Understanding the Topology and the Geometry of the Persistence Diagram Space via Optimal Partial Transport. - Vincent Divol; Théo Lacombe (CoRR, 2019)

A General Neural Network Architecture for Persistence Diagrams and Graph Classification. - Mathieu Carrière; Frédéric Chazal; Yuichi Ike; Théo Lacombe; Martin Royer; Yuhei Umeda (CoRR, 2019)

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. - Mathieu Carrière; Frédéric Chazal; Yuichi Ike; Théo Lacombe; Martin Royer; Yuhei Umeda (AISTATS, 2020)

Statistics for Topological Descriptors using optimal transport. (Statistiques sur les descripteurs topologiques à base de transport optimal). - Théo Lacombe (2020)

Estimation and Quantization of Expected Persistence Diagrams. - Vincent Divol; Théo Lacombe (ICML, 2021)

Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs. - Théo Lacombe; Yuichi Ike; Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuhei Umeda (IJCAI, 2021)

Understanding the topology and the geometry of the space of persistence diagrams via optimal partial transport. - Vincent Divol; Théo Lacombe (J. Appl. Comput. Topol., 2021)

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds. - Thibault de Surrel; Felix Hensel; Mathieu Carrière; Théo Lacombe; Yuichi Ike; Hiroaki Kurihara; Marc Glisse; Frédéric Chazal (TAG-ML, 2022)

An Homogeneous Unbalanced Regularized Optimal Transport Model with Applications to Optimal Transport with Boundary. - Théo Lacombe (AISTATS, 2023)

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks. - Felix Hensel; Charles Arnal; Mathieu Carrière; Théo Lacombe; Hiroaki Kurihara; Yuichi Ike; Frédéric Chazal (CoRR, 2023)

A gradient sampling algorithm for stratified maps with applications to topological data analysis. - Jacob Leygonie; Mathieu Carrière; Théo Lacombe; Steve Oudot (Math. Program., 2023)

Diffeomorphic interpolation for efficient persistence-based topological optimization. - Mathieu Carrière; Marc Theveneau; Théo Lacombe (NeurIPS, 2024)

Topological Node2vec: Enhanced Graph Embedding via Persistent Homology. - Yasuaki Hiraoka; Yusuke Imoto; Théo Lacombe; Killian Meehan; Toshiaki Yachimura (J. Mach. Learn. Res., 2024)

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks. - Charles Arnal; Felix Hensel; Mathieu Carrière; Théo Lacombe; Hiroaki Kurihara; Yuichi Ike; Frédéric Chazal (Trans. Mach. Learn. Res., 2024)

On the Existence of Monge Maps for the Gromov-Wasserstein Problem. - Théo Dumont; Théo Lacombe; François-Xavier Vialard (Found. Comput. Math., 2025)