5 citations · 6 across the 5 of their papers we have counts for
5 papers
Gaussian kernel expansion with basis functions uniformly bounded in
Mauro Bisiacco, Gianluigi Pillonetto
Kernel expansions are a topic of considerable interest in machine learning, also because of their relation to the so-called feature maps introduced in machine learning. Properties…
Kernel-based function learning in dynamic and non stationary environments
Alberto Giaretta, Mauro Bisiacco, Gianluigi Pillonetto
One central theme in machine learning is function estimation from sparse and noisy data. An example is supervised learning where the elements of the training set are couples, each…
Absolute integrability of Mercer kernels is only sufficient for RKHS stability
Mauro Bisiacco, Gianluigi Pillonetto
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces in one-to-one correspondence with positive definite maps called kernels. They are widely employed in machine le…
On the stability test for reproducing kernel Hilbert spaces
Mauro Bisiacco, Gianluigi Pillonetto
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces where all the evaluation functionals are linear and bounded. They are in one-to-one correspondence with positiv…
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…