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Filters: Author is Ernesto De Vito  [Clear All Filters]
2020
E. De Vito, M. Fornasier and V. Naumova. "A machine learning approach to optimal Tikhonov regularization I: Affine manifolds." Analysis and Applications (2020).PDF icon learning_parameter.pdf (472.12 KB)
2019
M. Fornasier, E. De Vito, Z. Kereta and V. Naumova. Automated parameter estimation for selected inverse problems In Grenoble, France., 2019.
E. De Vito, Z. Kereta, V. Naumova, L. Rosasco and S. Vigogna. Monte Carlo wavelets: a randomized approach to frame discretization In Sampling Theory and Applications. IEEE, 2019.
E. De Vito, Z. Kereta and V. Naumova. "Unsupervised parameter selection for denoising with the elastic net." Machine Learning (2019).
E. De Vito, Z. Kereta and V. Naumova. Unsupervised Parameter Selection in Variational Regularization. SPARS 2019, Toulouse, France, 2019.
2018
E. De Vito, Z. Kereta and V. Naumova. "A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net." arXiv (2018).PDF icon 1809.08696.pdf (1.75 MB)
2017
E. De Vito, Z. Kereta and V. Naumova. Nearly Optimal Parameter selection for the Elastic Net In Hangzhou, China., 2017.