From safe screening rules to working sets for faster Lasso-type solvers. - Mathurin Massias; Alexandre Gramfort; Joseph Salmon (CoRR, 2017)

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

Celer: a Fast Solver for the Lasso with Dual Extrapolation. - Mathurin Massias; Joseph Salmon; Alexandre Gramfort (ICML, 2018)

Learning step sizes for unfolded sparse coding. - Pierre Ablin; Thomas Moreau 001; Mathurin Massias; Alexandre Gramfort (NeurIPS, 2019)

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. - Quentin Bertrand; Mathurin Massias; Alexandre Gramfort; Joseph Salmon (NeurIPS, 2019)

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise. - Quentin Bertrand; Mathurin Massias; Alexandre Gramfort; Joseph Salmon (CoRR, 2019)

Dual Extrapolation for Sparse Generalized Linear Models. - Mathurin Massias; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (CoRR, 2019)

Support recovery and sup-norm convergence rates for sparse pivotal estimation. - Mathurin Massias; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon (AISTATS, 2020)

Implicit regularization for convex regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (CoRR, 2020)

Anderson acceleration of coordinate descent. - Quentin Bertrand; Mathurin Massias (AISTATS, 2021)

Iterative regularization for convex regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (AISTATS, 2021)

Dimension-free convergence rates for gradient Langevin dynamics in RKHS. - Boris Muzellec; Kanji Sato; Mathurin Massias; Taiji Suzuki (COLT, 2022)

Benchopt: Reproducible, efficient and collaborative optimization benchmarks. - Thomas Moreau 001; Mathurin Massias; Alexandre Gramfort; Pierre Ablin; Pierre-Antoine Bannier; Benjamin Charlier; Mathieu Dagréou; Tom Dupré la Tour; Ghislain Durif; Cássio F. Dantas; Quentin Klopfenstein; Johan Larsson 002; et al. (NeurIPS, 2022)

Beyond L1: Faster and Better Sparse Models with skglm. - Quentin Bertrand; Quentin Klopfenstein; Pierre-Antoine Bannier; Gauthier Gidel; Mathurin Massias (NeurIPS, 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 (J. Mach. Learn. Res., 2022)

Coordinate Descent for SLOPE. - Johan Larsson 002; Quentin Klopfenstein; Mathurin Massias; Jonas Wallin (AISTATS, 2023)

Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso. - Can Pouliquen; Paulo Gonçalves 001; Mathurin Massias; Titouan Vayer (CoRR, 2023)

Iterative regularization for low complexity regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (Numerische Mathematik, 2024)

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure. - Can Pouliquen; Mathurin Massias; Titouan Vayer (ICLR, 2025)

On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity. - Quentin Bertrand; Anne Gagneux; Mathurin Massias; Rémi Emonet (CoRR, 2025)

Proximal Operators of Sorted Nonconvex Penalties. - Anne Gagneux; Mathurin Massias; Emmanuel Soubies (CoRR, 2025)

Convexity in ReLU Neural Networks: Beyond ICNNs? - Anne Gagneux; Mathurin Massias; Emmanuel Soubies; Rémi Gribonval (J. Math. Imaging Vis., 2025)