Search Results for author:
Found 28 papers, 17 papers with code
Model order reduction of deep structured state-space models: A system-theoretic approach
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification.
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity.
BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Within this context, this paper focus on the cloud segmentation from remote sensing imagery.
In-context learning of state estimators
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
On the adaptation of in-context learners for system identification
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems.
Gradient-based bilevel optimization for multi-penalty Ridge regression through matrix differential calculus
Common regularization algorithms for linear regression, such as LASSO and Ridge regression, rely on a regularization hyperparameter that balances the tradeoff between minimizing the fitting error and the norm of the learned model coefficients.
Shedding Light on the Ageing of Extra Virgin Olive Oil: Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques
This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy.
Split-Boost Neural Networks
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results.
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances.
From system models to class models: An in-context learning paradigm
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class?
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones.
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent.
Learning Choice Functions with Gaussian Processes
We propose a Gaussian Process model to learn choice functions from choice-data.
Direct identification of continuous-time linear switched state-space models
The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block.
Learning neural state-space models: do we need a state estimator?
In recent years, several algorithms for system identification with neural state-space models have been introduced.
On the adaptation of recurrent neural networks for system identification
The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system.
Choice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A, B, C among this set of five options A, B, C, D, E''.
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features {\sl regardless} of the model used.
Deep learning with transfer functions: new applications in system identification
The dynamical operator is included as {the} last layer of a neural network in order to obtain the optimal one-step-ahead prediction error.
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
In a recent contribution we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning.
Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance
This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems.
Preferential Bayesian optimisation with Skew Gaussian Processes
In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation.
Continuous-time system identification with neural networks: Model structures and fitting criteria
This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems.
dynoNet: a neural network architecture for learning dynamical systems
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks.
Skew Gaussian Processes for Classification
In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model.
Model structures and fitting criteria for system identification with neural networks
This paper focuses on the identification of dynamical systems with tailor-made model structures, where neural networks are used to approximate uncertain components and domain knowledge is retained, if available.
Efficient Calibration of Embedded MPC
Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints.
Systems and Control Systems and Control Optimization and Control
Active preference learning based on radial basis functions
The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences.
