Omar Chehab

According to our database, Omar Chehab authored at least 15 papers between 2020 and 2026.

Collaborative distances:

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

2026

Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences.

Hanlin Yu, RuiKang OuYang, Partha Kaushik, Arto Klami, Michael U. Gutmann, Omar Chehab

CoRR, May, 2026

Nano World Models: A Minimalist Implementation of Future Video Prediction.

Siqiao Huang, Partha Kaushik, Michael Chen, Hengkai Pan, Kaiwen Geng, Omar Chehab, Fernando Moreno-Pino, Max Simchowitz

CoRR, May, 2026

MVICAD²: Multi-View Independent Component Analysis With Delays and Dilations.

Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort

IEEE Trans. Biomed. Eng., February, 2026

2025

Identifiable Multi-View Causal Discovery Without Non-Gaussianity.

Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyvärinen

CoRR, February, 2025

MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations.

Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort

CoRR, January, 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, January, 2025

Density Ratio Estimation with Conditional Probability Paths.

Hanlin Yu, Arto Klami, Aapo Hyvärinen, Anna Korba, Omar Chehab

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics.

Omar Chehab, Anna Korba, Austin J. Stromme, Adrien Vacher

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Practical Diffusion Path for Sampling.

Omar Chehab, Anna Korba

CoRR, 2024

2023

Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation.

Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen

CoRR, 2023

Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond.

Omar Chehab, Aapo Hyvärinen, Andrej Risteski

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

The optimal noise in noise-contrastive learning is not what you think.

Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen

Proceedings of the Uncertainty in Artificial Intelligence, 2022

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

Learning with self-supervision on EEG data.

Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann

Proceedings of the 9th International Winter Conference on Brain-Computer Interface, 2021

2020

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