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05/02/2023
Absolute integrability of Mercer kernels is only sufficient for RKHS stability
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces in ...
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05/01/2023
On the stability test for reproducing kernel Hilbert spaces
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces whe...
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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...
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01/30/2023
Deep networks for system identification: a Survey
Deep learning is a topic of considerable current interest. The availabil...
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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 ...
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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...
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05/06/2020
Mathematical foundations of stable RKHSs
Reproducing kernel Hilbert spaces (RKHSs) are key spaces for machine lea...
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09/05/2019
Kernel absolute summability is only sufficient for RKHS stability
Regularized approaches have been successfully applied to linear system i...
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05/20/2019
A novel Multiplicative Polynomial Kernel for Volterra series identification
Volterra series are especially useful for nonlinear system identificatio...
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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...
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08/01/2017
Distributed multi-agent Gaussian regression via Karhunen-Loève expansions
We consider the problem of distributedly estimating Gaussian random fiel...
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06/08/2017
The Generalized Cross Validation Filter
Generalized cross validation (GCV) is one of the most important approach...
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05/03/2017
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-tempor...
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12/29/2016
The interplay between system identification and machine learning
Learning from examples is one of the key problems in science and enginee...
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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...
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08/08/2016
Boosting as a kernel-based method
Boosting combines weak (biased) learners to obtain effective learning al...
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01/17/2016
On-line Bayesian System Identification
We consider an on-line system identification setting, in which new data ...
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08/12/2015
Maximum Entropy Vector Kernels for MIMO system identification
Recent contributions have framed linear system identification as a nonpa...
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07/02/2015
Identification of stable models via nonparametric prediction error methods
A new Bayesian approach to linear system identification has been propose...
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04/26/2015
Bayesian kernel-based system identification with quantized output data
In this paper we introduce a novel method for linear system identificati...
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11/21/2014
Robust EM kernel-based methods for linear system identification
Recent developments in system identification have brought attention to r...
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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...
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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 ...
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12/21/2013
Outlier robust system identification: a Bayesian kernel-based approach
In this paper, we propose an outlier-robust regularized kernel-based met...
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09/30/2013
Generalized system identification with stable spline kernels
Regularized least-squares approaches have been successfully applied to l...
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03/22/2013
Robust and Trend Following Student's t Kalman Smoothers
We present a Kalman smoothing framework based on modeling errors using t...
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03/12/2013
Linear system identification using stable spline kernels and PLQ penalties
The classical approach to linear system identification is given by param...
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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...
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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...
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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...
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01/19/2013
