Learning, Regularization and Ill-Posed Inverse Problems. - Lorenzo Rosasco; Andrea Caponnetto; Ernesto De Vito; Francesca Odone; Umberto De Giovannini (NIPS, 2004)

Some Properties of Regularized Kernel Methods - Ernesto De Vito; Lorenzo Rosasco; Andrea Caponnetto; Michele Piana; Alessandro Verri (J. Mach. Learn. Res., 2004)

Are Loss Functions All the Same?. - Lorenzo Rosasco; Ernesto De Vito; Andrea Caponnetto; Michele Piana; Alessandro Verri (Neural Comput., 2004)

Model Selection for Regularized Least-Squares Algorithm in Learning Theory. - Ernesto De Vito; Andrea Caponnetto; Lorenzo Rosasco (Found. Comput. Math., 2005)

Learning from Examples as an Inverse Problem. - Ernesto De Vito; Lorenzo Rosasco; Andrea Caponnetto; Umberto De Giovannini; Francesca Odone (J. Mach. Learn. Res., 2005)

Optimal Rates for the Regularized Least-Squares Algorithm. - Andrea Caponnetto; Ernesto De Vito (Found. Comput. Math., 2007)

Spectral Algorithms for Supervised Learning. - L. Lo Gerfo; Lorenzo Rosasco; Francesca Odone; Ernesto De Vito; Alessandro Verri (Neural Comput., 2008)

A Note on Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (COLT, 2009)

Entropy conditions for L r -convergence of empirical processes. - Andrea Caponnetto; Ernesto De Vito; Massimiliano Pontil (Adv. Comput. Math., 2009)

Elastic-net regularization in learning theory. - Christine De Mol; Ernesto De Vito; Lorenzo Rosasco (J. Complex., 2009)

Spectral Regularization for Support Estimation. - Ernesto De Vito; Lorenzo Rosasco; Alessandro Toigo (NIPS, 2010)

Adaptive Kernel Methods Using the Balancing Principle. - Ernesto De Vito; Sergei V. Pereverzyev; Lorenzo Rosasco (Found. Comput. Math., 2010)

On Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (J. Mach. Learn. Res., 2010)

A consistent algorithm to solve Lasso, elastic-net and Tikhonov regularization. - Ernesto De Vito; Veronica Umanità; Silvia Villa (J. Complex., 2011)

Geometrical and computational aspects of Spectral Support Estimation for novelty detection. - Alessandro Rudi; Francesca Odone; Ernesto De Vito (Pattern Recognit. Lett., 2014)

The Use of Representations in Applied Harmonic Analysis. - Filippo De Mari; Ernesto De Vito (Harmonic and Applied Analysis, 2015)

Enhancing Signal Discontinuities with Shearlets: An Application to Corner Detection. - Miguel A. Duval-Poo; Francesca Odone; Ernesto De Vito (ICIAP (2), 2015)

Edges and Corners With Shearlets. - Miguel A. Duval-Poo; Francesca Odone; Ernesto De Vito (IEEE Trans. Image Process., 2015)

Retinal Image Analysis with Shearlets. - Francesco Levet; Miguel A. Duval-Poo; Ernesto De Vito; Francesca Odone (STAG, 2016)

Scale Invariant Interest Points with Shearlets. - Miguel A. Duval-Poo; Nicoletta Noceti; Francesca Odone; Ernesto De Vito (CoRR, 2016)

Detecting Spatio-Temporally Interest Points Using the Shearlet Transform. - Damiano Malafronte; Francesca Odone; Ernesto De Vito (IbPRIA, 2017)

Regularized Kernel Algorithms for Support Estimation. - Alessandro Rudi; Ernesto De Vito; Alessandro Verri; Francesca Odone (Frontiers Appl. Math. Stat., 2017)

Scale Invariant and Noise Robust Interest Points With Shearlets. - Miguel A. Duval-Poo; Nicoletta Noceti; Francesca Odone; Ernesto De Vito (IEEE Trans. Image Process., 2017)

A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net. - Ernesto De Vito; Zeljko Kereta; Valeria Naumova (CoRR, 2018)

Space-Time Signal Analysis and the 3D Shearlet Transform. - Damiano Malafronte; Ernesto De Vito; Francesca Odone (J. Math. Imaging Vis., 2018)

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. - Ernesto De Vito; Nicole Mücke; Lorenzo Rosasco (CoRR, 2019)

Multi-Scale Vector Quantization with Reconstruction Trees. - Enrico Cecini; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2019)

Unitarization and Inversion Formulae for the Radon Transform Between Dual Pairs. - Giovanni S. Alberti; Francesca Bartolucci; Filippo De Mari; Ernesto De Vito (SIAM J. Math. Anal., 2019)

Regularized ERM on random subspaces. - Andrea Della Vecchia; Jaouad Mourtada; Ernesto De Vito; Lorenzo Rosasco (AISTATS, 2021)

Learning the optimal Tikhonov regularizer for inverse problems. - Giovanni S. Alberti; Ernesto De Vito; Matti Lassas; Luca Ratti; Matteo Santacesaria (NeurIPS, 2021)

Learning the optimal regularizer for inverse problems. - Giovanni S. Alberti; Ernesto De Vito; Matti Lassas; Luca Ratti; Matteo Santacesaria (CoRR, 2021)

Understanding neural networks with reproducing kernel Banach spaces. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2021)

Mean Nyström Embeddings for Adaptive Compressive Learning. - Antoine Chatalic; Luigi Carratino; Ernesto De Vito; Lorenzo Rosasco (AISTATS, 2022)

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

Multiclass learning with margin: exponential rates with no bias-variance trade-off. - Stefano Vigogna; Giacomo Meanti; Ernesto De Vito; Lorenzo Rosasco (ICML, 2022)

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. - Antoine Chatalic; Nicolas Schreuder; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2023)

Neural reproducing kernel Banach spaces and representer theorems for deep networks. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2024)

Learning sparsity-promoting regularizers for linear inverse problems. - Giovanni S. Alberti; Ernesto De Vito; Tapio Helin; Matti Lassas; Luca Ratti; Matteo Santacesaria (CoRR, 2024)

Learning convolution operators on compact Abelian groups. - Emilia Magnani; Ernesto De Vito; Philipp Hennig; Lorenzo Rosasco (CoRR, 2025)

Computational Efficiency under Covariate Shift in Kernel Ridge Regression. - Andrea Della Vecchia; Arnaud Mavakala Watusadisi; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2025)