Gianluigi Pillonetto

research

∙ 05/02/2023

Absolute integrability of Mercer kernels is only sufficient for RKHS stability

Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces in ...

0 Mauro Bisiacco, et al. ∙

research

∙ 05/01/2023

On the stability test for reproducing kernel Hilbert spaces

Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces whe...

0 Mauro Bisiacco, et al. ∙

research

∙ 02/21/2023

Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression

Many problems arising in control require the determination of a mathemat...

0 Wenqi Cao, et al. ∙

research

∙ 01/30/2023

Deep networks for system identification: a Survey

Deep learning is a topic of considerable current interest. The availabil...

0 Gianluigi Pillonetto, et al. ∙

research

∙ 03/25/2022

Dealing with collinearity in large-scale linear system identification using Bayesian regularization

We consider the identification of large-scale linear and stable dynamic ...

0 Wenqi Cao, et al. ∙

research

∙ 07/23/2021

Estimation of sparse linear dynamic networks using the stable spline horseshoe prior

Identification of the so-called dynamic networks is one of the most chal...

0 Gianluigi Pillonetto, et al. ∙

research

∙ 05/06/2020

Mathematical foundations of stable RKHSs

Reproducing kernel Hilbert spaces (RKHSs) are key spaces for machine lea...

0 Mauro Bisiacco, et al. ∙

research

∙ 09/05/2019

Kernel absolute summability is only sufficient for RKHS stability

Regularized approaches have been successfully applied to linear system i...

0 Mauro Bisiacco, et al. ∙

research

∙ 05/20/2019

A novel Multiplicative Polynomial Kernel for Volterra series identification

Volterra series are especially useful for nonlinear system identificatio...

0 Alberto Dalla Libera, et al. ∙

research

∙ 03/07/2018

Fast Robust Methods for Singular State-Space Models

State-space models are used in a wide range of time series analysis form...

0 Jonathan Jonker, et al. ∙

research

∙ 08/01/2017

Distributed multi-agent Gaussian regression via Karhunen-Loève expansions

We consider the problem of distributedly estimating Gaussian random fiel...

0 Gianluigi Pillonetto, et al. ∙

research

∙ 06/08/2017

The Generalized Cross Validation Filter

Generalized cross validation (GCV) is one of the most important approach...

0 Giulio Bottegal, et al. ∙

research

∙ 05/03/2017

Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering

In this work we study the non-parametric reconstruction of spatio-tempor...

0 Marco Todescato, et al. ∙

research

∙ 12/29/2016

The interplay between system identification and machine learning

Learning from examples is one of the key problems in science and enginee...

0 Gianluigi Pillonetto, et al. ∙

research

∙ 10/03/2016

A new kernel-based approach to system identification with quantized output data

In this paper we introduce a novel method for linear system identificati...

0 Giulio Bottegal, et al. ∙

research

∙ 08/08/2016

Boosting as a kernel-based method

Boosting combines weak (biased) learners to obtain effective learning al...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 01/17/2016

On-line Bayesian System Identification

We consider an on-line system identification setting, in which new data ...

0 Diego Romeres, et al. ∙

research

∙ 08/12/2015

Maximum Entropy Vector Kernels for MIMO system identification

Recent contributions have framed linear system identification as a nonpa...

0 Giulia Prando, et al. ∙

research

∙ 07/02/2015

Identification of stable models via nonparametric prediction error methods

A new Bayesian approach to linear system identification has been propose...

0 Diego Romeres, et al. ∙

research

∙ 04/26/2015

Bayesian kernel-based system identification with quantized output data

In this paper we introduce a novel method for linear system identificati...

0 Giulio Bottegal, et al. ∙

research

∙ 11/21/2014

Robust EM kernel-based methods for linear system identification

Recent developments in system identification have brought attention to r...

0 Giulio Bottegal, et al. ∙

research

∙ 09/29/2014

Bayesian and regularization approaches to multivariable linear system identification: the role of rank penalties

Recent developments in linear system identification have proposed the us...

0 Giulia Prando, et al. ∙

research

∙ 07/22/2014

Multi-agents adaptive estimation and coverage control using Gaussian regression

We consider a scenario where the aim of a group of agents is to perform ...

0 Andrea Carron, et al. ∙

research

∙ 12/21/2013

Outlier robust system identification: a Bayesian kernel-based approach

In this paper, we propose an outlier-robust regularized kernel-based met...

0 Giulio Bottegal, et al. ∙

research

∙ 09/30/2013

Generalized system identification with stable spline kernels

Regularized least-squares approaches have been successfully applied to l...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 03/22/2013

Robust and Trend Following Student's t Kalman Smoothers

We present a Kalman smoothing framework based on modeling errors using t...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 03/12/2013

Linear system identification using stable spline kernels and PLQ penalties

The classical approach to linear system identification is given by param...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 03/08/2013

Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation

In this paper, we present the optimization formulation of the Kalman fil...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 02/26/2013

Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso

The popular Lasso approach for sparse estimation can be derived via marg...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 01/22/2013

The connection between Bayesian estimation of a Gaussian random field and RKHS

Reconstruction of a function from noisy data is often formulated as a re...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 01/19/2013

Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory

We introduce a class of quadratic support (QS) functions, many of which ...

0 Aleksandr Y. Aravkin, et al. ∙

Gianluigi Pillonetto

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