Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning. - Alexandre Abraham; Elvis Dohmatob; Bertrand Thirion; Dimitris Samaras; Gaël Varoquaux (MICCAI (2), 2013)

Benchmarking solvers for TV-ℓ1 least-squares and logistic regression in brain imaging. - Elvis Dopgima Dohmatob; Alexandre Gramfort; Bertrand Thirion; Gaël Varoquaux (PRNI, 2014)

Region segmentation for sparse decompositions: better brain parcellations from rest fMRI. - Alexandre Abraham; Elvis Dohmatob; Bertrand Thirion; Dimitris Samaras; Gaël Varoquaux (CoRR, 2014)

Grouping Total Variation and Sparsity: Statistical Learning with Segmenting Penalties. - Michael Eickenberg; Elvis Dohmatob; Bertrand Thirion; Gaël Varoquaux (MICCAI (1), 2015)

Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease. - Mehdi Rahim; Bertrand Thirion; Alexandre Abraham; Michael Eickenberg; Elvis Dohmatob; Claude Comtat; Gaël Varoquaux (MICCAI (1), 2015)

Speeding-Up Model-Selection in Graphnet via Early-Stopping and Univariate Feature-Screening. - Elvis Dohmatob; Michael Eickenberg; Bertrand Thirion; Gaël Varoquaux (PRNI, 2015)

A simple and efficient algorithm for computing approximate Nash equilibria in two-person zero-sum sequential games with imcomplete information. - Elvis Dohmatob (CoRR, 2015)

FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging. - Gaël Varoquaux; Michael Eickenberg; Elvis Dohmatob; Bertrand Thirion (CoRR, 2015)

Local Q-linear convergence and finite-time active set identification of ADMM on a class of penalized regression problems. - Elvis Dohmatob; Michael Eickenberg; Bertrand Thirion; Gaël Varoquaux (ICASSP, 2016)

Multivariate hurst exponent estimation in FMRI. Application to brain decoding of perceptual learning. - Hubert Pelle; Philippe Ciuciu; Mehdi Rahim; Elvis Dohmatob; Patrice Abry; Virginie van Wassenhove (ISBI, 2016)

Learning brain regions via large-scale online structured sparse dictionary learning. - Elvis Dohmatob; Arthur Mensch; Gaël Varoquaux; Bertrand Thirion (NIPS, 2016)

Enhancement of functional brain connectome analysis by the use of deformable models in the estimation of spatial decompositions of the brain images. (Amélioration de connectivité fonctionnelle par utilisation de modèles déformables dans l'estimation de décompositions spatiales des images de cerveau). - Elvis Dohmatob (2017)

Limitations of adversarial robustness: strong No Free Lunch Theorem. - Elvis Dohmatob (CoRR, 2018)

Deep Determinantal Point Processes. - Mike Gartrell; Elvis Dohmatob (CoRR, 2018)

Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging. - Fouad Hadj-Selem; Tommy Löfstedt; Elvis Dohmatob; Vincent Frouin; Mathieu Dubois; Vincent Guillemot; Edouard Duchesnay (IEEE Trans. Medical Imaging, 2018)

Generalized No Free Lunch Theorem for Adversarial Robustness. - Elvis Dohmatob (ICML, 2019)

Learning Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Victor-Emmanuel Brunel; Elvis Dohmatob; Syrine Krichene (NeurIPS, 2019)

Distributionally Robust Reinforcement Learning. - Elena Smirnova; Elvis Dohmatob; Jérémie Mary (CoRR, 2019)

Adversarial Robustness via Adversarial Label-Smoothing. - Morgane Goibert; Elvis Dohmatob (CoRR, 2019)

On the Convergence of Approximate and Regularized Policy Iteration Schemes. - Elena Smirnova; Elvis Dohmatob (CoRR, 2019)

Distributionally Robust Counterfactual Risk Minimization. - Louis Faury; Ugo Tanielian; Elvis Dohmatob; Elena Smirnova; Flavian Vasile (AAAI, 2020)

Learning disconnected manifolds: a no GAN's land. - Ugo Tanielian; Thibaut Issenhuth; Elvis Dohmatob; Jérémie Mary (ICML, 2020)

On the Convergence of Smooth Regularized Approximate Value Iteration Schemes. - Elena Smirnova; Elvis Dohmatob (NeurIPS, 2020)

Learning disconnected manifolds: a no GANs land. - Ugo Tanielian; Thibaut Issenhuth; Elvis Dohmatob; Jérémie Mary (CoRR, 2020)

Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption. - Elvis Dohmatob (CoRR, 2020)

Implicit bias of any algorithm: bounding bias via margin. - Elvis Dohmatob (CoRR, 2020)

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Insu Han; Elvis Dohmatob; Jennifer Gillenwater; Victor-Emmanuel Brunel (ICLR, 2021)

Fundamental tradeoffs between memorization and robustness in random features and neural tangent regimes. - Elvis Dohmatob (CoRR, 2021)

Brain topography beyond parcellations: Local gradients of functional maps. - Elvis Dohmatob; Hugo Richard; Ana Luísa Pinho; Bertrand Thirion (NeuroImage, 2021)

Fast online ranking with fairness of exposure. - Nicolas Usunier; Virginie Do; Elvis Dohmatob (FAccT, 2022)

Scalable Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Jennifer Gillenwater; Elvis Dohmatob; Amin Karbasi (ICLR, 2022)

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Elvis Dohmatob; Amin Karbasi (ICML, 2022)

On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes. - Elvis Dohmatob; Alberto Bietti (CoRR, 2022)

An Adversarial Robustness Perspective on the Topology of Neural Networks. - Morgane Goibert; Thomas Ricatte; Elvis Dohmatob (CoRR, 2022)

Origins of Low-Dimensional Adversarial Perturbations. - Elvis Dohmatob; Chuan Guo 001; Morgane Goibert (AISTATS, 2023)

Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. - Meyer Scetbon; Elvis Dohmatob (AISTATS, 2023)

Contextual bandits with concave rewards, and an application to fair ranking. - Virginie Do; Elvis Dohmatob; Matteo Pirotta; Alessandro Lazaric; Nicolas Usunier (ICLR, 2023)

Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms. - Elvis Dohmatob; Meyer Scetbon (CoRR, 2023)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexis Thual; Alisha Kodibagkar; Amadeus Kanaan; et al. (Zenodo, 2023)

Scaling Laws for Associative Memories. - Vivien Cabannes; Elvis Dohmatob; Alberto Bietti (ICLR, 2024)

Consistent Adversarially Robust Linear Classification: Non-Parametric Setting. - Elvis Dohmatob (ICML, 2024)

A Tale of Tails: Model Collapse as a Change of Scaling Laws. - Elvis Dohmatob; Yunzhen Feng; Pu Yang; François Charton; Julia Kempe (ICML, 2024)

Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms. - Elvis Dohmatob; Meyer Scetbon (ICML, 2024)

Model Collapse Demystified: The Case of Regression. - Elvis Dohmatob; Yunzhen Feng; Julia Kempe (NeurIPS, 2024)

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement. - Yunzhen Feng; Elvis Dohmatob; Pu Yang; François Charton; Julia Kempe (CoRR, 2024)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexis Thual; Alisha Kodibagkar; Amadeus Kanaan; et al. (Zenodo, 2024)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexis Thual; Alisha Kodibagkar; Amadeus Kanaan; et al. (Zenodo, 2024)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexandre Sayal; Alexis Thual; Alisha Kodibagkar; et al. (Zenodo, 2024)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexandre Sayal; Alexis Thual; Alisha Kodibagkar; et al. (Zenodo, 2024)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexandre Sayal; Alexis Thual; Alisha Kodibagkar; et al. (Zenodo, 2024)

The Pitfalls of Memorization: When Memorization Hurts Generalization. - Reza Bayat; Mohammad Pezeshki; Elvis Dohmatob; David Lopez-Paz; Pascal Vincent (ICLR, 2025)

Strong Model Collapse. - Elvis Dohmatob; Yunzhen Feng; Arjun Subramonian; Julia Kempe (ICLR, 2025)

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification. - Yunzhen Feng; Elvis Dohmatob; Pu Yang; François Charton; Julia Kempe (ICLR, 2025)

An Effective Theory of Bias Amplification. - Arjun Subramonian; Samuel J. Bell; Levent Sagun; Elvis Dohmatob (ICLR, 2025)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexandre Sayal; Alexis Thual; Alisha Kodibagkar; et al. (Zenodo, 2025)

nilearn. - Nilearn Contributors; Ahmad Chamma; Aina Frau-Pascual; Alex Rothberg; Alexandre Abadie; Alexandre Abraham; Alexandre Gramfort; Alexandre Savio; Alexandre Cionca; Alexandre Sayal; Alexis Thual; Alisha Kodibagkar; et al. (Zenodo, 2025)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice. - Reyhane Askari Hemmat; Mohammad Pezeshki; Elvis Dohmatob; Florian Bordes; Pietro Astolfi; Melissa Hall; Jakob Verbeek; Michal Drozdzal; Adriana Romero-Soriano (CoRR, 2025)

auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory. - Arjun Subramonian; Elvis Dohmatob (CoRR, 2025)