Ernesto De Vito

48 papers A* 3A 3Misc 1Journal 36Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Sparsity-Promoting Regularizers for Linear Inverse Problems. SIAM J. Math. Data Sci. Giovanni S. Alberti, Ernesto De Vito, Tapio Helin, Matti Lassas, Luca Ratti, Matteo Santacesaria
2025	J	`jnl`	Computational Efficiency under Covariate Shift in Kernel Ridge Regression. CoRR Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco
2025	J	`jnl`	Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. J. Mach. Learn. Res. Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco
2025	J	`jnl`	Learning Multi-Index Models with Hyper-Kernel Ridge Regression. CoRR Shuo Huang, Hippolyte Labarrière, Ernesto De Vito, Tomaso A. Poggio, Lorenzo Rosasco
2025	J	`jnl`	Learning convolution operators on compact Abelian groups. CoRR Emilia Magnani, Ernesto De Vito, Philipp Hennig, Lorenzo Rosasco
2024	J	`jnl`	Learning sparsity-promoting regularizers for linear inverse problems. CoRR Giovanni S. Alberti, Ernesto De Vito, Tapio Helin, Matti Lassas, Luca Ratti, Matteo Santacesaria
2024	J	`jnl`	Neural reproducing kernel Banach spaces and representer theorems for deep networks. CoRR Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco, Stefano Vigogna
2024	J	`jnl`	The Nyström method for convex loss functions. J. Mach. Learn. Res. Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada, Lorenzo Rosasco
2023	J	`jnl`	Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. CoRR Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco
2022	A	`conf`	Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. AISTATS Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco
2022	J	`jnl`	Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. CoRR Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco
2022	A	`conf`	Mean Nyström Embeddings for Adaptive Compressive Learning. AISTATS Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco
2022	A*	`conf`	Multiclass learning with margin: exponential rates with no bias-variance trade-off. ICML Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco
2022	J	`jnl`	Multiclass learning with margin: exponential rates with no bias-variance trade-off. CoRR Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco
2021	A*	`conf`	Learning the optimal Tikhonov regularizer for inverse problems. NeurIPS Giovanni S. Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria
2021	J	`jnl`	Learning the optimal regularizer for inverse problems. CoRR Giovanni S. Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria
2021	J	`jnl`	Mean Nyström Embeddings for Adaptive Compressive Learning. CoRR Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco
2021	A	`conf`	Regularized ERM on random subspaces. AISTATS Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco
2021	J	`jnl`	Understanding neural networks with reproducing kernel Banach spaces. CoRR Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco, Stefano Vigogna
2020	J	`jnl`	Regularized ERM on random subspaces. CoRR Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco
2019	J	`jnl`	Multi-Scale Vector Quantization with Reconstruction Trees. CoRR Enrico Cecini, Ernesto De Vito, Lorenzo Rosasco
2019	J	`jnl`	Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. CoRR Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco
2019	J	`jnl`	Unitarization and Inversion Formulae for the Radon Transform Between Dual Pairs. SIAM J. Math. Anal. Giovanni S. Alberti, Francesca Bartolucci, Filippo De Mari, Ernesto De Vito
2018	J	`jnl`	A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net. CoRR Ernesto De Vito, Zeljko Kereta, Valeria Naumova
2018	J	`jnl`	Space-Time Signal Analysis and the 3D Shearlet Transform. J. Math. Imaging Vis. Damiano Malafronte, Ernesto De Vito, Francesca Odone
2017	Misc	`conf`	Detecting Spatio-Temporally Interest Points Using the Shearlet Transform. IbPRIA Damiano Malafronte, Francesca Odone, Ernesto De Vito
2017	J	`jnl`	Regularized Kernel Algorithms for Support Estimation. Frontiers Appl. Math. Stat. Alessandro Rudi, Ernesto De Vito, Alessandro Verri, Francesca Odone
2017	J	`jnl`	Scale Invariant and Noise Robust Interest Points With Shearlets. IEEE Trans. Image Process. Miguel A. Duval-Poo, Nicoletta Noceti, Francesca Odone, Ernesto De Vito
2016	—	`conf`	Retinal Image Analysis with Shearlets. STAG Francesco Levet, Miguel A. Duval-Poo, Ernesto De Vito, Francesca Odone
2016	J	`jnl`	Scale Invariant Interest Points with Shearlets. CoRR Miguel A. Duval-Poo, Nicoletta Noceti, Francesca Odone, Ernesto De Vito
2015	J	`jnl`	Edges and Corners With Shearlets. IEEE Trans. Image Process. Miguel A. Duval-Poo, Francesca Odone, Ernesto De Vito
2015	—	`conf`	Enhancing Signal Discontinuities with Shearlets: An Application to Corner Detection. ICIAP (2) Miguel A. Duval-Poo, Francesca Odone, Ernesto De Vito
2015	—	`ch.`	The Use of Representations in Applied Harmonic Analysis. Harmonic and Applied Analysis Filippo De Mari, Ernesto De Vito
2014	J	`jnl`	Geometrical and computational aspects of Spectral Support Estimation for novelty detection. Pattern Recognit. Lett. Alessandro Rudi, Francesca Odone, Ernesto De Vito
2011	J	`jnl`	A consistent algorithm to solve Lasso, elastic-net and Tikhonov regularization. J. Complex. Ernesto De Vito, Veronica Umanità, Silvia Villa
2010	J	`jnl`	Adaptive Kernel Methods Using the Balancing Principle. Found. Comput. Math. Ernesto De Vito, Sergei V. Pereverzyev, Lorenzo Rosasco
2010	J	`jnl`	On Learning with Integral Operators. J. Mach. Learn. Res. Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito
2010	—	`conf`	Spectral Regularization for Support Estimation. NIPS Ernesto De Vito, Lorenzo Rosasco, Alessandro Toigo
2009	A*	`conf`	A Note on Learning with Integral Operators. COLT Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito
2009	J	`jnl`	Elastic-net regularization in learning theory. J. Complex. Christine De Mol, Ernesto De Vito, Lorenzo Rosasco
2009	J	`jnl`	Entropy conditions for Adv. Comput. Math. Andrea Caponnetto, Ernesto De Vito, Massimiliano Pontil
2008	J	`jnl`	Spectral Algorithms for Supervised Learning. Neural Comput. L. Lo Gerfo, Lorenzo Rosasco, Francesca Odone, Ernesto De Vito, Alessandro Verri
2007	J	`jnl`	Optimal Rates for the Regularized Least-Squares Algorithm. Found. Comput. Math. Andrea Caponnetto, Ernesto De Vito
2005	J	`jnl`	Learning from Examples as an Inverse Problem. J. Mach. Learn. Res. Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone
2005	J	`jnl`	Model Selection for Regularized Least-Squares Algorithm in Learning Theory. Found. Comput. Math. Ernesto De Vito, Andrea Caponnetto, Lorenzo Rosasco
2004	J	`jnl`	Are Loss Functions All the Same?. Neural Comput. Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, Alessandro Verri
2004	—	`conf`	Learning, Regularization and Ill-Posed Inverse Problems. NIPS Lorenzo Rosasco, Andrea Caponnetto, Ernesto De Vito, Francesca Odone, Umberto De Giovannini
2004	J	`jnl`	Some Properties of Regularized Kernel Methods J. Mach. Learn. Res. Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Michele Piana, Alessandro Verri