Gramfort, Alexandre

47 publications

TMLR 2026 Diffusion Posterior Sampling for Simulation-Based Inference in Tall Data Settings Julia Linhart, Gabriel Cardoso, Alexandre Gramfort, Sylvain Le Corff, Pedro L. C. Rodrigues

TMLR 2025 SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation on Diverse Modalities Yanis Lalou, Theo Gnassounou, Antoine Collas, Antoine de Mathelin, Oleksii Kachaiev, Ambroise Odonnat, Thomas Moreau, Alexandre Gramfort, Rémi Flamary

NeurIPS 2024 Emg2qwerty: A Large Dataset with Baselines for Touch Typing Using Surface Electromyography Viswanath Sivakumar, Jeffrey Seely, Alan Du, Sean R Bittner, Adam Berenzweig, Anuoluwapo Bolarinwa, Alexandre Gramfort, Michael I Mandel

NeurIPS 2024 Geodesic Optimization for Predictive Shift Adaptation on EEG Data Apolline Mellot, Antoine Collas, Sylvain Chevallier, Alexandre Gramfort, Denis A. Engemann

NeurIPS 2023 Convolution Monge Mapping Normalization for Learning on Sleep Data Théo Gnassounou, Rémi Flamary, Alexandre Gramfort

NeurIPSW 2023 Evaluating the Structure of Cognitive Tasks with Transfer Learning Bruno Aristimunha, Raphael Yokoingawa de Camargo, Walter Hugo Lopez Pinaya, Sylvain Chevallier, Alexandre Gramfort, Cédric Rommel

ICML 2023 FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau

NeurIPS 2023 L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference Julia Linhart, Alexandre Gramfort, Pedro Rodrigues

NeurIPS 2022 Benchopt: Reproducible, Efficient and Collaborative Optimization Benchmarks Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupre la Tour, Ghislain Durif, Cassio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter

ICLR 2022 CADDA: Class-Wise Automatic Differentiable Data Augmentation for EEG Signals Cédric Rommel, Thomas Moreau, Joseph Paillard, Alexandre Gramfort

NeurIPS 2022 Deep Invariant Networks with Differentiable Augmentation Layers Cédric Rommel, Thomas Moreau, Alexandre Gramfort

ICLR 2022 DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals Cédric Allain, Alexandre Gramfort, Thomas Moreau

JMLR 2022 Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

AutoML 2022 LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi

UAI 2022 The Optimal Noise in Noise-Contrastive Learning Is Not What You Think Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen

NeurIPS 2022 Toward a Realistic Model of Speech Processing in the Brain with Self-Supervised Learning Juliette Millet, Charlotte Caucheteux, Pierre Orhan, Yves Boubenec, Alexandre Gramfort, Ewan Dunbar, Christophe Pallier, Jean-Remi King

ICML 2021 Disentangling Syntax and Semantics in the Brain with Deep Networks Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King

NeurIPS 2021 HNPE: Leveraging Global Parameters for Neural Posterior Estimation Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort

MLOSS 2021 Mvlearn: Multiview Machine Learning in Python Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

MLOSS 2021 POT: Python Optimal Transport Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer

NeurIPS 2021 Shared Independent Component Analysis for Multi-Subject Neuroimaging Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen

ICML 2020 Debiased Sinkhorn Barycenters Hicham Janati, Marco Cuturi, Alexandre Gramfort

JMLR 2020 Dual Extrapolation for Sparse GLMs Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

ICML 2020 Implicit Differentiation of Lasso-Type Models for Hyperparameter Optimization Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

NeurIPS 2020 Modeling Shared Responses in Neuroimaging Studies Through MultiView ICA Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

AISTATS 2020 Spatio-Temporal Alignments: Optimal Transport Through Space and Time Hicham Janati, Marco Cuturi, Alexandre Gramfort

NeurIPS 2020 Statistical Control for Spatio-Temporal MEG/EEG Source Imaging with Desparsified Mutli-Task Lasso Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion

AISTATS 2020 Support Recovery and Sup-Norm Convergence Rates for Sparse Pivotal Estimation Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon

NeurIPS 2019 Handling Correlated and Repeated Measurements with the Smoothed Multivariate Square-Root Lasso Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon

NeurIPS 2019 Learning Step Sizes for Unfolded Sparse Coding Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort

NeurIPS 2019 Manifold-Regression to Predict from MEG/EEG Brain Signals Without Source Modeling David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann

AISTATS 2019 Stochastic Algorithms with Descent Guarantees for ICA Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach

AISTATS 2019 Wasserstein Regularization for Sparse Multi-Task Regression Hicham Janati, Marco Cuturi, Alexandre Gramfort

ICML 2018 Celer: A Fast Solver for the Lasso with Dual Extrapolation Mathurin Massias, Alexandre Gramfort, Joseph Salmon

AISTATS 2018 Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

NeurIPS 2018 Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort

AISTATS 2017 Anomaly Detection in Extreme Regions via Empirical MV-Sets on the Sphere Albert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin

JMLR 2017 Gap Safe Screening Rules for Sparsity Enforcing Penalties Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

NeurIPS 2017 Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort

JMLR 2017 On the Consistency of Ordinal Regression Methods Fabian Pedregosa, Francis Bach, Alexandre Gramfort

NeurIPS 2016 GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

NeurIPS 2015 GAP Safe Screening Rules for Sparse Multi-Task and Multi-Class Models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

ICML 2015 Mind the Duality Gap: Safer Rules for the Lasso Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

ICLR 2013 Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian G. Bénar, Théodore Papadopoulo

ICML 2012 Small-Sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering Gaël Varoquaux, Alexandre Gramfort, Bertrand Thirion

MLOSS 2011 Scikit-Learn: Machine Learning in Python Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay

NeurIPS 2010 Brain Covariance Selection: Better Individual Functional Connectivity Models Using Population Prior Gael Varoquaux, Alexandre Gramfort, Jean-baptiste Poline, Bertrand Thirion