53 citations · 151 across the 36 of their papers we have counts for
7 papers · 1 filter
Newton-Raphson Consensus for Distributed Convex Optimization
Damiano Varagnolo, Filippo Zanella, Angelo Cenedese +2
We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local…
Maximum Entropy Vector Kernels for MIMO system identification
Giulia Prando, Gianluigi Pillonetto, Alessandro Chiuso
Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on -type regularization which accounts for the stabili…
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso +2
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the ad…
Classical vs. Bayesian methods for linear system identification: point estimators and confidence sets
D. Romeres, G. Prando, G. Pillonetto +1
This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the st…
Identification of stable models via nonparametric prediction error methods
Diego Romeres, Gianluigi Pillonetto, Alessandro Chiuso
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimati…
Bayesian kernel-based system identification with quantized output data
Giulio Bottegal, Gianluigi Pillonetto, Håkan Hjalmarsson
In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covarianc…