Stable Topological Signatures for Points on 3D Shapes. - Mathieu Carrière; Steve Y. Oudot; Maks Ovsjanikov (Comput. Graph. Forum, 2015)

Structure and Stability of the 1-Dimensional Mapper. - Mathieu Carrière; Steve Oudot (SoCG, 2016)

Local Equivalence and Intrinsic Metrics between Reeb Graphs. - Mathieu Carrière; Steve Oudot (SoCG, 2017)

Sliced Wasserstein Kernel for Persistence Diagrams. - Mathieu Carrière; Marco Cuturi; Steve Oudot (ICML, 2017)

On metric and statistical properties of topological descriptors for geometric data. (Sur les propriétés métriques et statistiques des descripteurs topologiques pour les données géométriques). - Mathieu Carrière (2017)

On the Metric Distortion of Embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces. - Mathieu Carrière; Ulrich Bauer (CoRR, 2018)

Topological Data Analysis of Single-cell Hi-C Contact Maps. - Mathieu Carrière; Raul Rabadan (CoRR, 2018)

Structure and Stability of the One-Dimensional Mapper. - Mathieu Carrière; Steve Oudot (Found. Comput. Math., 2018)

Statistical Analysis and Parameter Selection for Mapper. - Mathieu Carrière; Bertrand Michel; Steve Oudot (J. Mach. Learn. Res., 2018)

On the Metric Distortion of Embedding Persistence Diagrams into Separable Hilbert Spaces. - Mathieu Carrière; Ulrich Bauer (SoCG, 2019)

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis. - Rachel Jeitziner; Mathieu Carrière; Jacques Rougemont; Steve Oudot; Kathryn Hess; Cathrin Brisken (Bioinform., 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)

Approximation of Reeb spaces with Mappers and Applications to Stochastic Filters. - Mathieu Carrière; Bertrand Michel (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)

Multiparameter Persistence Image for Topological Machine Learning. - Mathieu Carrière; Andrew J. Blumberg (NeurIPS, 2020)

MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data. - Andrew J. Blumberg; Mathieu Carrière; Michael A. Mandell; Raul Rabadan; Soledad Villar (CoRR, 2020)

A note on stochastic subgradient descent for persistence-based functionals: convergence and practical aspects. - Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuichi Ike; Hariprasad Kannan (CoRR, 2020)

Optimizing persistent homology based functions. - Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuichi Ike; Hariprasad Kannan; Yuhei Umeda (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)

Identifying homogeneous subgroups of patients and important features: a topological machine learning approach. - Ewan Carr; Mathieu Carrière; Bertrand Michel; Frédéric Chazal; Raquel Iniesta (BMC Bioinform., 2021)

Topology identifies emerging adaptive mutations in SARS-CoV-2. - Michael Bleher; Lukas Hahn; Juan Ángel Patino-Galindo; Mathieu Carrière; Ulrich Bauer; Raul Rabadan; Andreas Ott (CoRR, 2021)

Persistent homology based characterization of the breast cancer immune microenvironment: a feasibility study. - Andrew Aukerman; Mathieu Carrière; Chao Chen 012; Kevin Gardner; Raúl Rabadán; Rami Vanguri (J. Comput. Geom., 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)

Efficient Approximation of Multiparameter Persistence Modules. - David Loiseaux; Mathieu Carrière; Andrew J. Blumberg (CoRR, 2022)

Statistical analysis of Mapper for stochastic and multivariate filters. - Mathieu Carrière; Bertrand Michel (J. Appl. Comput. Topol., 2022)

A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions. - David Loiseaux; Mathieu Carrière; Andrew J. Blumberg (NeurIPS, 2023)

Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures. - David Loiseaux; Luis Scoccola; Mathieu Carrière; Magnus Bakke Botnan; Steve Oudot (NeurIPS, 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)

Differentiable Mapper for Topological Optimization of Data Representation. - Ziyad Oulhaj; Mathieu Carrière; Bertrand Michel (ICML, 2024)

Differentiability and Optimization of Multiparameter Persistent Homology. - Luis Scoccola; Siddharth Setlur; David Loiseaux; Mathieu Carrière; Steve Oudot (ICML, 2024)

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

Resampling and averaging coordinates on data. - Andrew J. Blumberg; Mathieu Carrière; Jun Hou Fung; Michael A. Mandell (CoRR, 2024)

Subsampling, aligning, and averaging to find circular coordinates in recurrent time series. - Andrew J. Blumberg; Mathieu Carrière; Jun Hou Fung; Michael A. Mandell (CoRR, 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)

Sparsification of the Generalized Persistence Diagrams for Scalability Through Gradient Descent. - Mathieu Carrière; Seunghyun Kim; Woojin Kim 001 (SoCG, 2025)