Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening. - Louis Béthune; Yacouba Kaloga; Pierre Borgnat; Aurélien Garivier; Amaury Habrard (Algorithms, 2020)

Predicting the Accuracy of a Few-Shot Classifier. - Myriam Bontonou; Louis Béthune; Vincent Gripon (CoRR, 2020)

Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs. - Carlos Lassance; Louis Béthune; Myriam Bontonou; Mounia Hamidouche; Vincent Gripon (CoRR, 2020)

The Many Faces of 1-Lipschitz Neural Networks. - Louis Béthune; Alberto González-Sanz; Franck Mamalet; Mathieu Serrurier (CoRR, 2021)

Predicting the Generalization Ability of a Few-Shot Classifier. - Myriam Bontonou; Louis Béthune; Vincent Gripon (Inf., 2021)

Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning. - Thomas Mullor; David Vigouroux; Louis Béthune (SEC, 2022)

Pay attention to your loss : understanding misconceptions about Lipschitz neural networks. - Louis Béthune; Thibaut Boissin; Mathieu Serrurier; Franck Mamalet; Corentin Friedrich; Alberto González-Sanz (NeurIPS, 2022)

GAN Estimation of Lipschitz Optimal Transport Maps. - Alberto González-Sanz; Lucas de Lara; Louis Béthune; Jean-Michel Loubes (CoRR, 2022)

Xplique: A Deep Learning Explainability Toolbox. - Thomas Fel; Lucas Hervier; David Vigouroux; Antonin Poché; Justin Plakoo; Rémi Cadène; Mathieu Chalvidal; Julien Colin; Thibaut Boissin; Louis Béthune; Agustin Martin Picard; Claire Nicodeme; et al. (CoRR, 2022)

When adversarial attacks become interpretable counterfactual explanations. - Mathieu Serrurier; Franck Mamalet; Thomas Fel; Louis Béthune; Thibaut Boissin (CoRR, 2022)

Gaussian Processes on Distributions based on Regularized Optimal Transport. - François Bachoc; Louis Béthune; Alberto González-Sanz; Jean-Michel Loubes (AISTATS, 2023)

CRAFT: Concept Recursive Activation FacTorization for Explainability. - Thomas Fel; Agustin Martin Picard; Louis Béthune; Thibaut Boissin; David Vigouroux; Julien Colin; Rémi Cadène; Thomas Serre (CVPR, 2023)

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. - Louis Béthune; Paul Novello; Guillaume Coiffier; Thibaut Boissin; Mathieu Serrurier; Quentin Vincenot; Andres Troya-Galvis (ICML, 2023)

A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation. - Thomas Fel; Victor Boutin; Louis Béthune; Rémi Cadène; Mazda Moayeri; Léo Andéol; Mathieu Chalvidal; Thomas Serre (NeurIPS, 2023)

On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective. - Mathieu Serrurier; Franck Mamalet; Thomas Fel; Louis Béthune; Thibaut Boissin (NeurIPS, 2023)

TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability. - Fanny Jourdan; Louis Béthune; Agustin Picard; Laurent Risser; Nicholas Asher (CoRR, 2023)

DP-SGD Without Clipping: The Lipschitz Neural Network Way. - Louis Béthune; Thomas Massena; Thibaut Boissin; Aurélien Bellet; Franck Mamalet; Yannick Prudent; Corentin Friedrich; Mathieu Serrurier; David Vigouroux (ICLR, 2024)

Understanding Visual Feature Reliance through the Lens of Complexity. - Thomas Fel; Louis Béthune; Andrew K. Lampinen; Thomas Serre; Katherine L. Hermann (NeurIPS, 2024)

Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions. - Yu-Guan Hsieh; Cheng-Yu Hsieh; Shih-Ying Yeh; Louis Béthune; Hadipour Ansari; Pavan Kumar Anasosalu Vasu; Chun-Liang Li; Ranjay Krishna; Oncel Tuzel; Marco Cuturi (CoRR, 2024)

Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models. - Michael Kirchhof; James Thornton; Pierre Ablin; Louis Béthune; Eugène Ndiaye; Marco Cuturi (CoRR, 2024)

Deep learning with Lipschitz constraints. (Apprentissage profond avec contraintes Lipschitz). - Louis Béthune (2024)

1-Lipschitz Neural Distance Fields. - Guillaume Coiffier; Louis Béthune (Comput. Graph. Forum, 2024)

Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo. - James Thornton; Louis Béthune; Ruixiang Zhang; Arwen Bradley; Preetum Nakkiran; Shuangfei Zhai (AISTATS, 2025)

Multimodal Autoregressive Pre-training of Large Vision Encoders. - Enrico Fini; Mustafa Shukor; Xiujun Li; Philipp Dufter; Michal Klein; David Haldimann; Sai Aitharaju; Victor G. Turrisi da Costa; Louis Béthune; Zhe Gan; Alexander Toshev; Marcin Eichner; et al. (CVPR, 2025)

Multivariate Conformal Prediction using Optimal Transport. - Michal Klein; Louis Béthune; Eugène Ndiaye; Marco Cuturi (CoRR, 2025)

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection. - Louis Béthune; David Grangier; Dan Busbridge; Eleonora Gualdoni; Marco Cuturi; Pierre Ablin (CoRR, 2025)

Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures. - Nina Vesseron; Louis Béthune; Marco Cuturi (CoRR, 2025)

Deep Sturm-Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions. - David Vigouroux; Joseba Dalmau; Louis Béthune; Victor Boutin (CoRR, 2025)

Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models. - Louis Béthune; David Vigouroux; Yilun Du; Rufin VanRullen; Thomas Serre; Victor Boutin (CoRR, 2025)

The Geometries of Truth Are Orthogonal Across Tasks. - Waïss Azizian; Michael Kirchhof; Eugène Ndiaye; Louis Béthune; Michal Klein; Pierre Ablin; Marco Cuturi (CoRR, 2025)

Scaling Laws for Optimal Data Mixtures. - Mustafa Shukor; Louis Béthune; Dan Busbridge; David Grangier; Enrico Fini; Alaaeldin El-Nouby; Pierre Ablin (CoRR, 2025)