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 | 865 | 2014 |
A new kernel-based approach for linear system identification G Pillonetto, G De Nicolao Automatica 46 (1), 81-93, 2010 | 521 | 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 | 243 | 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 | 222 | 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 | 205 | 2015 |
A Bayesian approach to sparse dynamic network identification A Chiuso, G Pillonetto Automatica 48 (8), 1553-1565, 2012 | 195 | 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 | 183 | 2014 |
System identification: A machine learning perspective A Chiuso, G Pillonetto Annual Review of Control, Robotics, and Autonomous Systems 2 (1), 281-304, 2019 | 130 | 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 | 127 | 2011 |
Generalized Kalman smoothing: Modeling and algorithms A Aravkin, JV Burke, L Ljung, A Lozano, G Pillonetto Automatica 86, 63-86, 2017 | 122 | 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 | 115 | 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 | 114 | 2003 |
Motion planning using adaptive random walks S Carpin, G Pillonetto IEEE Transactions on Robotics 21 (1), 129-136, 2005 | 112 | 2005 |
An-Laplace Robust Kalman Smoother AY Aravkin, BM Bell, JV Burke, G Pillonetto IEEE Transactions on Automatic Control 56 (12), 2898-2911, 2011 | 109 | 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 | 104 | 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 | 90 | 2015 |
Regularized system identification: Learning dynamic models from data G Pillonetto, T Chen, A Chiuso, G De Nicolao, L Ljung Springer Nature, 2022 | 86 | 2022 |
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 | 79 | 2013 |
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 | 78 | 2016 |
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 | 78 | 2008 |