Publications

• 2025
  • Q. Bertrand, A. Gagneux, M. Massias, R. Emonet, On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity, preprint.
  • A. Gagneux, M. Massias, E. Soubies, R. Gribonval, Convexity in ReLU Neural Networks: beyond ICNNs?, Journal of Mathematical Imaging and Vision, 2025.
  • C. Pouliquen, M. Massias, T. Vayer, Schur’s Positive-Definite Network: Deep Learning in the SPD cone with structure, ICLR 2025.
  • A. Gagneux, S. Martin, R. Emonet, Q. Bertrand, M. Massias A Visual Dive into Conditional Flow Matching, blogpost track @ICLR2025.
• 2024
  • A. Gagneux, M. Massias, E. Soubies, Proximal Operators of Sorted Nonconvex Penalties, submitted.
• 2023
  • J. Larsson, Q. Klopfenstein, M. Massias, J. Wallin, Coordinate descent for SLOPE, AISTATS 2023.
  • B. Moufad, P.-A. Bannier, Q. Bertrand, Q. Klopfenstein, M. Massias, skglm: improving scikit-learn for regularized Generalized Linear Models, submitted to JMLR OSS.
  • C. Molinari, M. Massias, L. Rosasco, S. Villa, Iterative regularization for low-complexity regularizers, Numerische Mathematike.
  • C. Pouliquen, P. Gonçalves, M. Massias, T. Vayer, Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso, GRETSI 2023.
• 2022
  • T. Moreau, M. Massias, A. Gramfort and others, Benchopt: Reproducible, efficient and collaborative optimization benchmarks, NeuRIPS 2022.
  • Q. Bertrand, Q. Klopfenstein, P.-A. Bannier, G. Gidel, M. Massias, Beyond L1: Faster and better sparse models with skglm, NeuRIPS 2022.
  • B. Muzellec, K. Sato, M. Massias, T. Suzuki, Dimension-free convergence rates for gradient Langevin dynamics in RKHS, COLT 2022.
• 2021
  • Q. Bertrand, Q. Klopfenstein, M. Massias, M. Blondel, S. Vaiter, A. Gramfort, J. Salmon, Implicit differentiation for fast hyperparameter selection in non-smooth convex learning, JMLR. code
  • Q. Bertrand, M. Massias, Anderson acceleration of coordinate descent, AISTATS 2021. code
  • C. Molinari, M. Massias, L. Rosasco, S. Villa, Iterative regularization for convex regularizers, AISTATS 2021. code
• 2020
  • M. Massias*, Q. Bertrand*, A. Gramfort, J. Salmon, Support recovery and sup-norm convergence rates for sparse pivotal estimation, AISTATS 2020.
  • M. Massias, S. Vaiter, A. Gramfort, J. Salmon, Dual extrapolation for sparse Generalized Linear Models, JMLR. celer library
• 2019
  • P. Ablin, T. Moreau, M. Massias, A. Gramfort, Learning step sizes for unfolded sparse coding, NeurIPS 2019.
  • Q. Bertrand*, M. Massias*, A. Gramfort, J. Salmon, Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso, NeurIPS 2019. code
• 2018
  • M. Massias, A. Gramfort, J. Salmon, Celer: a fast solver for the Lasso with dual extrapolation, ICML 2018. doc
  • M. Massias, O. Fercoq, A. Gramfort, J. Salmon, Generalized concomitant multi-task Lasso for sparse multimodal regression, AISTATS 2018.
• 2017
  • M. Massias, A. Gramfort, J. Salmon, From safe screening rules to working sets for faster Lasso-type solvers, OPTML workshop at NIPS 2017.
  • M. Massias, J. Salmon, A. Gramfort, Gap safe screening rules for faster complex-valued multi-task group Lasso, SPARS, Lisbon, 2017.

PhD. Thesis

• M. Massias, Sparse high dimension linear regression in the presence of heteroscedastic noise: application to magnetoelectric source imaging. Defended on 04/12/2019. manuscript slides