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09/06/2023
Split-Boost Neural Networks
The calibration and training of a neural network is a complex and time-c...
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08/25/2023
In-context learning for model-free system identification
In traditional system identification, we estimate a model of an unknown ...
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04/13/2023
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification
Effective quantification of uncertainty is an essential and still missin...
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02/27/2023
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Black-box optimization refers to the optimization problem whose objectiv...
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02/01/2023
Learning Choice Functions with Gaussian Processes
In consumer theory, ranking available objects by means of preference rel...
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06/26/2022
Learning neural state-space models: do we need a state estimator?
In recent years, several algorithms for system identification with neura...
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01/21/2022
On the adaptation of recurrent neural networks for system identification
This paper presents a transfer learning approach which enables fast and ...
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10/15/2021
Choice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian o...
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07/24/2021
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
This paper presents the intrinsic limit determination algorithm (ILD Alg...
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04/20/2021
Deep learning with transfer functions: new applications in system identification
This paper presents a linear dynamical operator described in terms of a ...
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12/12/2020
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-N...
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08/15/2020
Preferential Bayesian optimisation with Skew Gaussian Processes
Bayesian optimisation (BO) is a very effective approach for sequential b...
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06/03/2020
dynoNet: a neural network architecture for learning dynamical systems
This paper introduces a network architecture, called dynoNet, utilizing ...
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06/03/2020
Continuous-time system identification with neural networks: model structures and fitting criteria
This paper presents tailor-made neural model structures and two custom f...
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05/26/2020
Skew Gaussian Processes for Classification
Gaussian processes (GPs) are distributions over functions, which provide...
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11/29/2019
Model structures and fitting criteria for system identification with neural networks
This paper focuses on the identification of dynamical systems with tailo...
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09/28/2019
