| 1 |
KERNEL ABSOLUTE SUMMABILITY IS SUFFICIENT BUT NOT NECESSARY FOR RKHS STABILITY
Bisiacco M, Pillonetto G
SIAM Journal on Control and Optimization, 58(4), 2006, 2020 |
| 2 |
System identification using kernel-based regularization: New insights on stability and consistency issues
Pillonetto G
Automatica, 93, 321, 2018 |
| 3 |
On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses
Chen TS, Pillonetto G
Automatica, 95, 529, 2018 |
| 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 |
KERNELS FOR LINEAR TIME INVARIANT SYSTEM IDENTIFICATION
Dinuzzo F
SIAM Journal on Control and Optimization, 53(5), 3299, 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 |
Consistent identification of Wiener systems: A machine learning viewpoint
Pillonetto G
Automatica, 49(9), 2704, 2013 |
| 8 |
Regularized spectrum estimation using stable spline kernels
Bottegal G, Pillonetto G
Automatica, 49(11), 3199, 2013 |
| 9 |
Distributed parametric and nonparametric regression with on-line performance bounds computation
Varagnolo D, Pillonetto G, Schenato L
Automatica, 48(10), 2468, 2012 |
| 10 |
Stochastic control with imperfect models
Basu A, Borkar VS
SIAM Journal on Control and Optimization, 47(3), 1274, 2008 |
