53 citations · 151 across the 36 of their papers we have counts for
5 papers · 1 filter
The interplay between system identification and machine learning
Gianluigi Pillonetto
Learning from examples is one of the key problems in science and engineering. It deals with function reconstruction from a finite set of direct and noisy samples. Regularization in…
A new kernel-based approach to system identification with quantized output data
Giulio Bottegal, Håkan Hjalmarsson, Gianluigi Pillonetto
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…
Generalized Kalman Smoothing: Modeling and Algorithms
A. Y. Aravkin, J. V. Burke, L. Ljung +2
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions,…
Boosting as a kernel-based method
Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto
Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-…
On-line Bayesian System Identification
Diego Romeres, Giulia Prando, Gianluigi Pillonetto +1
We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailo…