3 citations · 4 across the 10 of their papers we have counts for
3 papers · 1 filter
Learning stochasticity: a nonparametric framework for intrinsic noise estimation
Gianluigi Pillonetto, Alberto Giaretta, Mauro Bisiacco
Understanding the principles that govern dynamical systems is a central challenge across many scientific domains, including biology and ecology. Incomplete knowledge of nonlinear i…
Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology
G. Pillonetto, A. Giaretta, A. Aravkin +2
Data-driven discovery of model equations is a powerful approach for understanding the behavior of dynamical systems in many scientific fields. In particular, the ability to learn m…
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…