5 papers
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…
On-line learning of dynamic systems: sparse regression meets Kalman filtering
Gianluigi Pillonetto, Akram Yazdani, Aleksandr Aravkin
Learning governing equations from data is central to understanding the behavior of physical systems across diverse scientific disciplines, including physics, biology, and engineeri…
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…
The Bayesian Separation Principle for Data-driven Control
Giacomo Baggio, Ruggero Carli, Riccardo Alessandro Grimaldi +1
In this paper we investigate the existence of a separation principle between model identification and control design in the context of model predictive control. First, we clarify t…
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…