| 1 |
On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses
Chen TS, Pillonetto G
Automatica, 95, 529, 2018 |
| 2 |
Sparse plus low rank network identification: A nonparametric approach
Zorzi M, Chiuso A
Automatica, 76, 355, 2017 |
| 3 |
Maximum Entropy vector kernels for MIMO system identification
Prando G, Chiuso A, Pillonetto G
Automatica, 79, 326, 2017 |
| 4 |
Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
Pillonetto G, Chen TS, Chiuso A, De Nicolao G, Ljung L
Automatica, 69, 137, 2016 |
| 5 |
Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator
Pillonetto G, Chiuso A
Automatica, 58, 106, 2015 |
| 6 |
Kernel methods in system identification, machine learning and function estimation: A survey
Pillonetto G, Dinuzzo F, Chen TS, De Nicolao G, Ljung L
Automatica, 50(3), 657, 2014 |
| 7 |
A novel methodology for non-linear system identification of battery cells used in non-road hybrid electric vehicles
Unger J, Hametner C, Jakubek S, Quasthoff M
Journal of Power Sources, 269, 883, 2014 |
| 8 |
A Bayesian approach to sparse dynamic network identification
Chiuso A, Pillonetto G
Automatica, 48(8), 1553, 2012 |
| 9 |
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Pillonetto G, Chiuso A, De Nicolao G
Automatica, 47(2), 291, 2011 |
| 10 |
A new kernel-based approach for linear system identification
Pillonetto G, De Nicolao G
Automatica, 46(1), 81, 2010 |
