De Vito, Ernesto

13 publications

NeurIPS 2025 Computational Efficiency Under Covariate Shift in Kernel Ridge Regression Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco

JMLR 2025 Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco

JMLR 2024 The Nyström Method for Convex Loss Functions Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada, Lorenzo Rosasco

AISTATS 2022 Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco

AISTATS 2022 Mean Nyström Embeddings for Adaptive Compressive Learning Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco

ICML 2022 Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco

AISTATS 2021 Regularized ERM on Random Subspaces Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco

NeurIPS 2021 Learning the Optimal Tikhonov Regularizer for Inverse Problems Giovanni S Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria

JMLR 2010 On Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

COLT 2009 A Note on Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

JMLR 2005 Learning from Examples as an Inverse Problem Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone

NeCo 2004 Are Loss Functions All the Same? Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, Alessandro Verri

JMLR 2004 Some Properties of Regularized Kernel Methods Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Michele Piana, Alessandro Verri