| Convergence rates of Forward–Douglas–Rachford splitting method C Molinari, J Liang, J Fadili Journal of Optimization Theory and Applications 182, 606-639, 2019 | 23 | 2019 |
| Generalized conditional gradient with augmented lagrangian for composite minimization A Silveti-Falls, C Molinari, J Fadili SIAM Journal on Optimization 30 (4), 2687-2725, 2020 | 21 | 2020 |
| Zeroth-order optimization with orthogonal random directions D Kozak, C Molinari, L Rosasco, L Tenorio, S Villa Mathematical Programming 199 (1), 1179-1219, 2023 | 19 | 2023 |
| Iterative regularization for convex regularizers C Molinari, M Massias, L Rosasco, S Villa International conference on artificial intelligence and statistics, 1684-1692, 2021 | 18 | 2021 |
| Inexact and stochastic generalized conditional gradient with augmented lagrangian and proximal step A Silveti-Falls, C Molinari, J Fadili Journal of Nonsmooth Analysis and Optimization 2 (Original research articles), 2021 | 11 | 2021 |
| Alternating forward–backward splitting for linearly constrained optimization problems C Molinari, J Peypouquet, F Roldan Optimization Letters 14 (5), 1071-1088, 2020 | 11 | 2020 |
| Lagrangian Penalization Scheme with Parallel Forward–Backward Splitting C Molinari, J Peypouquet Journal of Optimization Theory and Applications 177, 413-447, 2018 | 10 | 2018 |
| An optimal structured zeroth-order algorithm for non-smooth optimization M Rando, C Molinari, L Rosasco, S Villa Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| A stochastic Bregman primal-dual splitting algorithm for composite optimization A Silveti-Falls, C Molinari, J Fadili arXiv preprint arXiv:2112.11928, 2021 | 9 | 2021 |
| Iterative regularization for low complexity regularizers C Molinari, M Massias, L Rosasco, S Villa Numerische Mathematik 156 (2), 641-689, 2024 | 6 | 2024 |
| Stochastic zeroth order descent with structured directions M Rando, C Molinari, S Villa, L Rosasco arXiv preprint arXiv:2206.05124, 2022 | 6 | 2022 |
| Optimal distributed control of linear parabolic equations by spectral decomposition M Lazar, C Molinari Optimal Control Applications and Methods 42 (4), 891-926, 2021 | 4 | 2021 |
| Optimal control of parabolic equations by spectral decomposition M Lazar, C Molinari, J Peypouquet Optimization 66 (8), 1359-1381, 2017 | 4 | 2017 |
| Fast iterative regularization by reusing data C Vega, C Molinari, L Rosasco, S Villa Journal of Inverse and Ill-posed Problems 32 (4), 733-759, 2024 | 2 | 2024 |
| Linear quadratic control of nonlinear systems with Koopman operator learning and the Nystr\" om method E Caldarelli, A Chatalic, A Colomé, C Molinari, C Ocampo-Martinez, ... arXiv preprint arXiv:2403.02811, 2024 | 1 | 2024 |
| On Learning the Optimal Regularization Parameter in Inverse Problems J CHIRINOS-RODRÍGUEZ, E De Vito, C Molinari, L Rosasco, S Villa arXiv e-prints, arXiv: 2311.15845, 2023 | 1 | 2023 |
| On learning the optimal regularization parameter in inverse problems JC Rodriguez, E De Vito, C Molinari, L Rosasco, S Villa arXiv preprint arXiv:2311.15845, 2023 | 1 | 2023 |
| Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems H Kleikamp, M Lazar, C Molinari arXiv preprint arXiv:2307.15590, 2023 | 1 | 2023 |
| Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques C Sartore, M Rando, G Romualdi, C Molinari, L Rosasco, D Pucci arXiv preprint arXiv:2409.18649, 2024 | | 2024 |
| A Supervised Learning Approach to Regularization of Inverse Problems JC Rodriguez, E De Vito, C Molinari, L Rosasco, S Villa arXiv preprint arXiv:2311.15845, 2023 | | 2023 |
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Universitŕ degli Studi di GenovaVerified email at unige.it
