| Implicit differentiation of lasso-type models for hyperparameter optimization Q Bertrand, Q Klopfenstein, M Blondel, S Vaiter, A Gramfort, J Salmon International Conference on Machine Learning, 810-821, 2020 | 85 | 2020 |
| Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning S Lachapelle, T Deleu, D Mahajan, I Mitliagkas, Y Bengio, ... ICML 2023, 2023 | 44 | 2023 |
| On the stability of iterative retraining of generative models on their own data Q Bertrand, AJ Bose, A Duplessis, M Jiralerspong, G Gidel ICLR 2024, 2023 | 42 | 2023 |
| Implicit differentiation for fast hyperparameter selection in non-smooth convex learning Q Bertrand, Q Klopfenstein, M Massias, M Blondel, S Vaiter, A Gramfort, ... Journal of Machine Learning Research 23 (149), 1-43, 2022 | 40 | 2022 |
| On the Limitations of Elo: Real-World Games, are Transitive, not Additive Q Bertrand, WM Czarnecki, G Gidel AISTATS 2023, 2023 | 23 | 2023 |
| Anderson acceleration of coordinate descent Q Bertrand, M Massias International Conference on Artificial Intelligence and Statistics, 1288-1296, 2021 | 23 | 2021 |
| Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso Q Bertrand, M Massias, A Gramfort, J Salmon Advances in Neural Information Processing Systems 32, 2019 | 23 | 2019 |
| Beyond L1: Faster and Better Sparse Models with skglm Q Bertrand, Q Klopfenstein, PA Bannier, G Gidel, M Massias Advances in Neural Information Processing Systems, 2022 | 18 | 2022 |
| The Curse of Unrolling: Rate of Differentiating Through Optimization D Scieur, Q Bertrand, G Gidel, F Pedregosa Advances in Neural Information Processing Systems, 2022 | 15 | 2022 |
| Local linear convergence of proximal coordinate descent algorithm Q Klopfenstein, Q Bertrand, A Gramfort, J Salmon, S Vaiter Optimization Letters 18 (1), 135-154, 2024 | 10* | 2024 |
| Support recovery and sup-norm convergence rates for sparse pivotal estimation M Massias, Q Bertrand, A Gramfort, J Salmon International Conference on Artificial Intelligence and Statistics, 2655-2665, 2020 | 9* | 2020 |
| Self-consuming generative models with curated data provably optimize human preferences D Ferbach, Q Bertrand, AJ Bose, G Gidel arXiv preprint arXiv:2407.09499, 2024 | 8 | 2024 |
| Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma Q Bertrand, J Duque, E Calvano, G Gidel arXiv preprint arXiv:2312.08484, 2023 | 6 | 2023 |
| Omega: Optimistic EMA Gradients J Ramirez, R Sukumaran, Q Bertrand, G Gidel ICML 2023 LatinX in AI Workshop, 2023 | 4 | 2023 |
| Dimension improvement in Dhar's refutation of the Eden conjecture Q Bertrand, J Pertinand Physics Letters A 382 (11), 761-765, 2018 | 4 | 2018 |
| Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning PA Bannier, Q Bertrand, J Salmon, A Gramfort Medical Imaging Meets NeurIPS Workshop, 2021 | 1 | 2021 |
| Hyperparameter selection for high dimensional sparse learning: application to neuroimaging Q Bertrand Université Paris-Saclay, 2021 | 1 | 2021 |
| Anytime Exact Belief Propagation G Azevedo Ferreira, Q Bertrand, C Maussion, R de Salvo Braz AAAI-17 Workshop on Symbolic Inference and Optimization (SymInfOpt-17), 2017 | 1* | 2017 |