Search Results for author:
Found 32 papers, 1 papers with code
Gaussian kernel expansion with basis functions uniformly bounded in $\mathcal{L}_{\infty}$
Motivated by this line of research, we investigate under this constraint all possible kernel expansions of the Gaussian kernel, one of the most widely used models in machine learning.
The Bayesian Separation Principle for Data-driven Control
This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control.
Kernel-based function learning in dynamic and non stationary environments
This includes the important exploration-exploitation problems where e. g. a set of agents/robots has to monitor an environment to reconstruct a sensorial field and their movements rules are continuously updated on the basis of the acquired knowledge on the field and/or the surrounding environment.
Absolute integrability of Mercer kernels is only sufficient for RKHS stability
Working in continuous-time, it is the purpose of this note to prove that the same result holds also for Mercer kernels.
On the stability test for reproducing kernel Hilbert spaces
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces where all the evaluation functionals are linear and bounded.
Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression
Many problems arising in control require the determination of a mathematical model of the application.
Deep networks for system identification: a Survey
For this reason, we provide a survey of deep learning from a system identification perspective.
Dealing with collinearity in large-scale linear system identification using Bayesian regularization
We consider the identification of large-scale linear and stable dynamic systems whose outputs may be the result of many correlated inputs.
Sliding-mode theory under feedback constraints and the problem of epidemic control
It is only apparently confined to the linear setting and allows also to study an important set of nonlinear models.
COVID-19 epidemic control using short-term lockdowns for collective gain
In this paper we show that the Australian model can be generalized and given a rigorous mathematical analysis, casting strategies of the type short-term pain for collective gain in the context of sliding-mode control, an important branch of nonlinear control theory.
Mathematical foundations of stable RKHSs
Overall, our new results provide novel mathematical foundations of stable RKHSs with impact on stability tests, impulse responses modeling and computational efficiency of regularized schemes for linear system identification.
Kernel absolute summability is only sufficient for RKHS stability
Many of them model unknown impulse responses exploiting the so called Reproducing Kernel Hilbert spaces (RKHSs) that enjoy the notable property of being in one-to-one correspondence with the class of positive semidefinite kernels.
A novel Multiplicative Polynomial Kernel for Volterra series identification
Volterra series are especially useful for nonlinear system identification, also thanks to their capability to approximate a broad range of input-output maps.
Fast Robust Methods for Singular State-Space Models
We therefore suggest that the proposed approach be the {\it default choice} for estimating state space models outside of the Gaussian context, regardless of whether the error covariances are singular or not.
The Generalized Cross Validation Filter
Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques.
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data.
The interplay between system identification and machine learning
One reason is that the RKHSs usually employed in machine learning do not embed the information available on dynamic systems, e. g. BIBO stability.
A new kernel-based approach to system identification with quantized output data
In this paper we introduce a novel method for linear system identification with quantized output data.
Boosting as a kernel-based method
We show that boosting with this learner is equivalent to estimation with a special {\it boosting kernel} that depends on $K$, as well as on the regression matrix, noise variance, and hyperparameters.
On-line Bayesian System Identification
We consider an on-line system identification setting, in which new data become available at given time steps.
Maximum Entropy Vector Kernels for MIMO system identification
In this paper, adopting Maximum Entropy arguments, we derive a new $\ell_2$ penalty deriving from a vector-valued kernel; to do so we exploit the structure of the Hankel matrix, thus controlling at the same time complexity, measured by the McMillan degree, stability and smoothness of the identified models.
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
In this paper, a comparative study of estimators based on these different types of regularizers is reported.
Identification of stable models via nonparametric prediction error methods
Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system.
Bayesian kernel-based system identification with quantized output data
Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data.
Robust EM kernel-based methods for linear system identification
In this paper, we introduce a novel method to robustify kernel-based system identification methods.
Bayesian and regularization approaches to multivariable linear system identification: the role of rank penalties
Recent developments in linear system identification have proposed the use of non-parameteric methods, relying on regularization strategies, to handle the so-called bias/variance trade-off.
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 the optimal coverage of a region according to a sensory function.
Outlier robust system identification: a Bayesian kernel-based approach
In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification.
Generalized system identification with stable spline kernels
This paper extends linear system identification to a wide class of nonsmooth stable spline estimators, where regularization functionals and data misfits can be selected from a rich set of piecewise linear-quadratic (PLQ) penalties.
The connection between Bayesian estimation of a Gaussian random field and RKHS
Reconstruction of a function from noisy data is often formulated as a regularized optimization problem over an infinite-dimensional reproducing kernel Hilbert space (RKHS).
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 play a crucial role in a variety of applications, including machine learning, robust statistical inference, sparsity promotion, and Kalman smoothing.
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors
We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems.
