| Understanding neural networks with reproducing kernel Banach spaces F Bartolucci, E De Vito, L Rosasco, S Vigogna Applied and Computational Harmonic Analysis 62, 194-236, 2023 | 58 | 2023 |
| Coorbit spaces with voice in a Fréchet space S Dahlke, F De Mari, E De Vito, D Labate, G Steidl, G Teschke, S Vigogna Journal of Fourier Analysis and Applications 23, 141-206, 2017 | 29 | 2017 |
| Multiscale regression on unknown manifolds W Liao, M Maggioni, S Vigogna arXiv preprint arXiv:2101.05119, 2021 | 13 | 2021 |
| Learning adaptive multiscale approximations to data and functions near low-dimensional sets W Liao, M Maggioni, S Vigogna 2016 IEEE Information Theory Workshop (ITW), 226-230, 2016 | 12 | 2016 |
| Park: Sound and efficient kernel ridge regression by feature space partitions L Carratino, S Vigogna, D Calandriello, L Rosasco Advances in Neural Information Processing Systems 34, 6430-6441, 2021 | 10 | 2021 |
| Estimating multi-index models with response-conditional least squares T Klock, A Lanteri, S Vigogna | 9 | 2021 |
| Intrinsic Localization of Anisotropic Frames II: -Molecules P Grohs, S Vigogna Journal of Fourier Analysis and Applications 21 (1), 182-205, 2015 | 8 | 2015 |
| A quantitative functional central limit theorem for shallow neural networks V Cammarota, D Marinucci, M Salvi, S Vigogna Modern Stochastics: Theory and Applications 11 (1), 85-108, 2023 | 7 | 2023 |
| Conditional regression for single-index models A Lanteri, M Maggioni, S Vigogna Bernoulli 28 (4), 3051-3078, 2022 | 6 | 2022 |
| Neural reproducing kernel Banach spaces and representer theorems for deep networks F Bartolucci, E De Vito, L Rosasco, S Vigogna arXiv preprint arXiv:2403.08750, 2024 | 4 | 2024 |
| How many samples are needed to leverage smoothness? V Cabannes, S Vigogna Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| A case of exponential convergence rates for SVM V Cabannnes, S Vigogna International Conference on Artificial Intelligence and Statistics, 359-374, 2023 | 4 | 2023 |
| Multiclass learning with margin: exponential rates with no bias-variance trade-off S Vigogna, G Meanti, E De Vito, L Rosasco International Conference on Machine Learning, 22260-22269, 2022 | 3 | 2022 |
| Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel E De Vito, Z Kereta, V Naumova, L Rosasco, S Vigogna Journal of Fourier Analysis and Applications 27, 1-39, 2021 | 3 | 2021 |
| Continuous and discrete frames generated by the evolution flow of the Schrödinger equation GS Alberti, S Dahlke, F De Mari, E De Vito, S Vigogna Analysis and Applications 15 (06), 915-937, 2017 | 3 | 2017 |
| Monte Carlo wavelets: a randomized approach to frame discretization Z Kereta, S Vigogna, V Naumova, L Rosasco, E De Vito 2019 13th International conference on Sampling Theory and Applications …, 2019 | 2 | 2019 |
| A Biased Kaczmarz Algorithm for Clustered Equations A Lanteri, M Maggioni, S Vigogna New Statistical Developments in Data Science: SIS 2017, Florence, Italy …, 2019 | 1 | 2019 |
| A Lipschitz spaces view of infinitely wide shallow neural networks F Bartolucci, M Carioni, JA Iglesias, Y Korolev, E Naldi, S Vigogna arXiv preprint arXiv:2410.14591, 2024 | | 2024 |
| Spectral complexity of deep neural networks S Di Lillo, D Marinucci, M Salvi, S Vigogna arXiv preprint arXiv:2405.09541, 2024 | | 2024 |
| Geometric classification of semidirect products in the maximal parabolic subgroup of F De Mari, E De Vito, S Vigogna Analysis and Applications 15 (02), 241-259, 2017 | | 2017 |