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Kernel methods in system identification, machine learning and function estimation: A survey
Learning techniques tailored to the specific features of dynamic systems may outperform conventional parametric approaches for identification of stable linear systems. Expand
A new kernel-based approach for linear system identification
This paper describes a new kernel-based approach for linear system identification of stable systems. Expand
Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework
We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. Expand
Newton-Raphson Consensus for Distributed Convex Optimization
We address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints and wants to collaborate to compute the minimizer of the sum of the local costs. Expand
A Bayesian approach to sparse dynamic network identification
We introduce two new nonparametric techniques which borrow ideas from a recently introduced kernel estimator called ''stable-spline'' as well as from sparsity inducing priors which use @?"1-type penalties. Expand
Learning Output Kernels with Block Coordinate Descent
We propose a method to learn simultaneously a vector-valued function and a kernel between its components. Expand
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
A novel Bayesian paradigm for the identification of output error models has recently been proposed in which, in place of postulating finite-dimensional models of the system transfer function, the system impulse response is searched for within an infinite-dimensional space. Expand
Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation.
The so-called minimal model (MM) of glucose kinetics is widely employed to estimate insulin sensitivity (S(I)) both in clinical and epidemiological studies. Usually, MM is numerically identified byExpand
Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator
The aim of this work is to provide new insights on the stable spline estimator equipped with ML estimation of hyperparameters. Expand
Bayesian Online Multitask Learning of Gaussian Processes
We derive an efficient computational scheme for an important class of multitask kernels. Expand