Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy. - Marylou Gabrié; Eric W. Tramel; Florent Krzakala (NIPS, 2015)

Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy. - Marylou Gabrié; Eric W. Tramel; Florent Krzakala (CoRR, 2015)

Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines. - Eric W. Tramel; Andre Manoel; Francesco Caltagirone; Marylou Gabrié; Florent Krzakala (ITW, 2016)

Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16). - Vinayak Abrol; Olivier Absil; Pierre-Antoine Absil; Sandrine Anthoine; Philippe Antoine; Thomas Arildsen; Nancy Bertin; Folkert Bleichrodt; Jérôme Bobin; Anne Bol; Antoine Bonnefoy; Francesco Caltagirone; et al. (CoRR, 2016)

Phase transitions in the $q$-coloring of random hypergraphs. - Marylou Gabrié; Varsha Dani; Guilhem Semerjian; Lenka Zdeborová (CoRR, 2017)

A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines. - Eric W. Tramel; Marylou Gabrié; Andre Manoel; Francesco Caltagirone; Florent Krzakala (CoRR, 2017)

Entropy and mutual information in models of deep neural networks. - Marylou Gabrié; Andre Manoel; Clément Luneau; Jean Barbier; Nicolas Macris; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2018)

Blind Calibration for Sparse Regression: A State Evolution Analysis. - Marylou Gabrié; Jean Barbier; Florent Krzakala; Lenka Zdeborová (CAMSAP, 2019)

Blind calibration for compressed sensing: State evolution and an online algorithm. - Marylou Gabrié; Jean Barbier; Florent Krzakala; Lenka Zdeborová (CoRR, 2019)

Mean-field inference methods for neural networks. - Marylou Gabrié (CoRR, 2019)

Towards an understanding of neural networks: mean-field incursions. (Éléments de compréhension des réseaux de neurones pour l'apprentissage automatique par méthodes de champ moyen). - Marylou Gabrié (2019)

Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging. - Hannah Lawrence; David Barmherzig; Henry Li; Michael Eickenberg; Marylou Gabrié (MSML, 2021)

On the interplay between data structure and loss function in classification problems. - Stéphane d'Ascoli; Marylou Gabrié; Levent Sagun; Giulio Biroli (NeurIPS, 2021)

More data or more parameters? Investigating the effect of data structure on generalization. - Stéphane d'Ascoli; Marylou Gabrié; Levent Sagun; Giulio Biroli (CoRR, 2021)

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Marylou Gabrié; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2021)

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. - Marylou Gabrié; Grant M. Rotskoff; Eric Vanden-Eijnden (CoRR, 2021)

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. - James A. Brofos; Marylou Gabrié; Marcus A. Brubaker; Roy R. Lederman (AISTATS, 2022)

Local-Global MCMC kernels: the best of both worlds. - Sergey Samsonov; Evgeny Lagutin; Marylou Gabrié; Alain Durmus; Alexey Naumov; Eric Moulines (NeurIPS, 2022)

On Sampling with Approximate Transport Maps. - Louis Grenioux; Alain Oliviero Durmus; Eric Moulines; Marylou Gabrié (ICML, 2023)

Balanced Training of Energy-Based Models with Adaptive Flow Sampling. - Louis Grenioux; Éric Moulines; Marylou Gabrié (CoRR, 2023)

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX. - Kaze W. K. Wong; Marylou Gabrié; Daniel Foreman-Mackey (J. Open Source Softw., 2023)

Stochastic Localization via Iterative Posterior Sampling. - Louis Grenioux; Maxence Noble; Marylou Gabrié; Alain Oliviero Durmus (ICML, 2024)

A theoretical perspective on mode collapse in variational inference. - Roman Soletskyi; Marylou Gabrié; Bruno Loureiro (CoRR, 2024)

Learned Reference-based Diffusion Sampling for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (CoRR, 2024)

Learned Reference-based Diffusion Sampler for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (ICLR, 2025)

Sampling metastable systems using collective variables and Jarzynski-Crooks paths. - Christoph Schönle; Marylou Gabrié; Tony Lelièvre; Gabriel Stoltz (J. Comput. Phys., 2025)

Improving the evaluation of samplers on multi-modal targets. - Louis Grenioux; Maxence Noble; Marylou Gabrié (CoRR, 2025)