Uncovering the structure of clinical EEG signals with self-supervised learning. - Hubert J. Banville; Omar Chehab; Aapo Hyvärinen; Denis-Alexander Engemann; Alexandre Gramfort (CoRR, 2020)

Learning with self-supervision on EEG data. - Alexandre Gramfort; Hubert J. Banville; Omar Chehab; Aapo Hyvärinen; Denis A. Engemann (BCI, 2021)

Deep Recurrent Encoder: A scalable end-to-end network to model brain signals. - Omar Chehab; Alexandre Défossez; Jean-Christophe Loiseau; Alexandre Gramfort; Jean-Remi King (CoRR, 2021)

The optimal noise in noise-contrastive learning is not what you think. - Omar Chehab; Alexandre Gramfort; Aapo Hyvärinen (UAI, 2022)

Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. - Omar Chehab; Aapo Hyvärinen; Andrej Risteski (NeurIPS, 2023)

Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. - Omar Chehab; Alexandre Gramfort; Aapo Hyvärinen (CoRR, 2023)

A Practical Diffusion Path for Sampling. - Omar Chehab; Anna Korba (CoRR, 2024)

Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics. - Omar Chehab; Anna Korba; Austin J. Stromme; Adrien Vacher (ICLR, 2025)

Polynomial time sampling from log-smooth distributions in fixed dimension under semi-log-concavity of the forward diffusion with application to strongly dissipative distributions. - Adrien Vacher; Omar Chehab; Anna Korba (CoRR, 2025)

MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations. - Ambroise Heurtebise; Omar Chehab; Pierre Ablin; Alexandre Gramfort (CoRR, 2025)

Density Ratio Estimation with Conditional Probability Paths. - Hanlin Yu; Arto Klami; Aapo Hyvärinen; Anna Korba; Omar Chehab (CoRR, 2025)

Identifiable Multi-View Causal Discovery Without Non-Gaussianity. - Ambroise Heurtebise; Omar Chehab; Pierre Ablin; Alexandre Gramfort; Aapo Hyvärinen (CoRR, 2025)