Gianluigi Pillonetto
Gianluigi Pillonetto
Professor of Automatic Control, University of Padova
Verified email at - Homepage
Cited by
Cited by
Kernel methods in system identification, machine learning and function estimation: A survey
G Pillonetto, F Dinuzzo, T Chen, G De Nicolao, L Ljung
Automatica 50 (3), 657-682, 2014
A new kernel-based approach for linear system identification
G Pillonetto, G De Nicolao
Automatica 46 (1), 81-93, 2010
Prediction error identification of linear systems: a nonparametric Gaussian regression approach
G Pillonetto, A Chiuso, G De Nicolao
Automatica 47 (2), 291-305, 2011
Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework
G Quer, R Masiero, G Pillonetto, M Rossi, M Zorzi
IEEE Transactions on wireless communications 11 (10), 3447-3461, 2012
Newton-Raphson consensus for distributed convex optimization
D Varagnolo, F Zanella, A Cenedese, G Pillonetto, L Schenato
IEEE Transactions on Automatic Control 61 (4), 994-1009, 2015
A Bayesian approach to sparse dynamic network identification
A Chiuso, G Pillonetto
Automatica 48 (8), 1553-1565, 2012
System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques
T Chen, MS Andersen, L Ljung, A Chiuso, G Pillonetto
IEEE Transactions on Automatic Control 59 (11), 2933-2945, 2014
System identification: A machine learning perspective
A Chiuso, G Pillonetto
Annual Review of Control, Robotics, and Autonomous Systems 2, 281-304, 2019
A new kernel-based approach for nonlinearsystem identification
G Pillonetto, MH Quang, A Chiuso
IEEE Transactions on Automatic Control 56 (12), 2825-2840, 2011
Generalized Kalman smoothing: Modeling and algorithms
A Aravkin, JV Burke, L Ljung, A Lozano, G Pillonetto
Automatica 86, 63-86, 2017
Newton-Raphson consensus for distributed convex optimization
F Zanella, D Varagnolo, A Cenedese, G Pillonetto, L Schenato
2011 50th IEEE Conference on Decision and Control and European Control …, 2011
Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation
G Pillonetto, G Sparacino, C Cobelli
Mathematical biosciences 184 (1), 53-67, 2003
Motion planning using adaptive random walks
S Carpin, G Pillonetto
IEEE Transactions on Robotics 21 (1), 129-136, 2005
An-Laplace Robust Kalman Smoother
AY Aravkin, BM Bell, JV Burke, G Pillonetto
IEEE Transactions on Automatic Control 56 (12), 2898-2911, 2011
Learning output kernels with block coordinate descent
F Dinuzzo, CS Ong, G Pillonetto, PV Gehler
Proceedings of the 28th international conference on machine learning (icml …, 2011
Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator
G Pillonetto, A Chiuso
Automatica 58, 106-117, 2015
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory.
AY Aravkin, JV Burke, G Pillonetto
Journal of Machine Learning Research 14, 2013
Bayesian online multitask learning of Gaussian processes
G Pillonetto, F Dinuzzo, G De Nicolao
IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (2), 193-205, 2008
Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
G Pillonetto, T Chen, A Chiuso, G De Nicolao, L Ljung
Automatica 69, 137-149, 2016
Minimal model SI=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals
G Pillonetto, G Sparacino, P Magni, R Bellazzi, C Cobelli
American Journal of Physiology-Endocrinology and Metabolism 282 (3), E564-E573, 2002
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