Anytime Exact Belief Propagation. - Gabriel Azevedo Ferreira; Quentin Bertrand; Charles Maussion; Rodrigo de Salvo Braz (CoRR, 2017)

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)

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

Implicit differentiation of Lasso-type models for hyperparameter optimization. - Quentin Bertrand; Quentin Klopfenstein; Mathieu Blondel; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (ICML, 2020)

Model identification and local linear convergence of coordinate descent. - Quentin Klopfenstein; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon; Samuel Vaiter (CoRR, 2020)

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

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

The Curse of Unrolling: Rate of Differentiating Through Optimization. - Damien Scieur; Gauthier Gidel; Quentin Bertrand; Fabian Pedregosa (NeurIPS, 2022)

Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. - Sébastien Lachapelle; Tristan Deleu; Divyat Mahajan; Ioannis Mitliagkas; Yoshua Bengio; Simon Lacoste-Julien; Quentin Bertrand (CoRR, 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)

On the Limitations of the Elo, Real-World Games are Transitive, not Additive. - Quentin Bertrand; Wojciech Marian Czarnecki; Gauthier Gidel (AISTATS, 2023)

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. - Sébastien Lachapelle; Tristan Deleu; Divyat Mahajan; Ioannis Mitliagkas; Yoshua Bengio; Simon Lacoste-Julien; Quentin Bertrand (ICML, 2023)

Omega: Optimistic EMA Gradients. - Juan Ramirez; Rohan Sukumaran; Quentin Bertrand; Gauthier Gidel (CoRR, 2023)

Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma. - Quentin Bertrand; Juan Duque; Emilio Calvano; Gauthier Gidel (CoRR, 2023)

On the Stability of Iterative Retraining of Generative Models on their own Data. - Quentin Bertrand; Avishek Joey Bose; Alexandre Duplessis; Marco Jiralerspong; Gauthier Gidel (ICLR, 2024)

Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences. - Damien Ferbach; Quentin Bertrand; Avishek Joey Bose; Gauthier Gidel (NeurIPS, 2024)

Local linear convergence of proximal coordinate descent algorithm. - Quentin Klopfenstein; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon; Samuel Vaiter (Optim. Lett., 2024)

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