Marylou Gabrié

According to our database, Marylou Gabrié authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learned Reference-based Diffusion Sampling for multi-modal distributions.

Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus

CoRR, 2024

A theoretical perspective on mode collapse in variational inference.

Roman Soletskyi, Marylou Gabrié, Bruno Loureiro

CoRR, 2024

Stochastic Localization via Iterative Posterior Sampling.

Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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., March, 2023

Balanced Training of Energy-Based Models with Adaptive Flow Sampling.

Louis Grenioux, Éric Moulines, Marylou Gabrié

CoRR, 2023

On Sampling with Approximate Transport Maps.

Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié

Proceedings of the International Conference on Machine Learning, 2023

2022

Local-Global MCMC kernels: the best of both worlds.

Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals.

James A. Brofos, Marylou Gabrié, Marcus A. Brubaker, Roy R. Lederman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods.

Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden

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

More data or more parameters? Investigating the effect of data structure on generalization.

Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli

CoRR, 2021

On the interplay between data structure and loss function in classification problems.

Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Proceedings of the Mathematical and Scientific Machine Learning, 2021

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

PhD thesis, 2019

Mean-field inference methods for neural networks.

Marylou Gabrié

CoRR, 2019

Blind calibration for compressed sensing: State evolution and an online algorithm.

Marylou Gabrié, Jean Barbier, Florent Krzakala, Lenka Zdeborová

CoRR, 2019

Blind Calibration for Sparse Regression: A State Evolution Analysis.

Marylou Gabrié, Jean Barbier, Florent Krzakala, Lenka Zdeborová

Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019

2018

Entropy and mutual information in models of deep neural networks.

Marylou Gabrié, Andre Manoel, Clément Luneau, Jean Barbier, Nicolas Macris, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Phase transitions in the $q$-coloring of random hypergraphs.

Marylou Gabrié, Varsha Dani, Guilhem Semerjian, Lenka Zdeborová

CoRR, 2017

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, Valerio Cambareri, Cecile Chenot, Vladimir S. Crnojevic, Marie Danková, Kévin Degraux, Jens Eisert, Mohamed-Jalal Fadili, Marylou Gabrié, Nicolas Gac, Daniele Giacobello, Carlos A. Gomez Gonzalez, Adriana Gonzalez, Pierre-Yves Gousenbourger, Mads Græsbøll Christensen, Rémi Gribonval, Stéphanie Guérit, Shaoguang Huang, Paul Irofti, Laurent Jacques, Ulugbek S. Kamilov, Srdan Kitic, Martin Kliesch, Florent Krzakala, John A. Lee, Wenzhi Liao, Tobias Lindstrøm Jensen, Andre Manoel, Hassan Mansour, Ali Mohammad-Djafari, Amirafshar Moshtaghpour, Fred Maurice Ngolè Mboula, Benoît Pairet, Marko Panic, Gabriel Peyré, Aleksandra Pizurica, Pavel Rajmic, Matthieu Roblin, Ingo Roth, Anil Kumar Sao, Pulkit Sharma, Jean-Luc Starck, Eric W. Tramel, Toon van Waterschoot, Dejan Vukobratovic, Li Wang, Benedikt Wirth, Gerhard Wunder, Hongyan Zhang

CoRR, 2016

Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines.

Eric W. Tramel, Andre Manoel, Francesco Caltagirone, Marylou Gabrié, Florent Krzakala

Proceedings of the 2016 IEEE Information Theory Workshop, 2016

2015

Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy.

Marylou Gabrié, Eric W. Tramel, Florent Krzakala

CoRR, 2015

Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy.

Marylou Gabrié, Eric W. Tramel, Florent Krzakala

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015