5 papers
Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
Jun Chen, Umberto Biccari, Junmin Wang
We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadratic Regulator (LQR) problems by…
Operator learning for prescribed-time stabilization of reaction-diffusion systems
Kaijing Lyu, Umberto Biccari, Jun-Min Wang
This paper addresses boundary prescribed-time stabilization of a one-dimensional heat equation with spatially and temporally varying coefficients. In contrast to asymptotic or expo…
Fair feature attribution for multi-output prediction: a Shapley-based perspective
Umberto Biccari, Alain Ibáñez de Opakua, José María Mato +3
In this article, we provide an axiomatic characterization of feature attribution for multi-output predictors within the Shapley framework. While SHAP explanations are routinely com…
Spiking Neural Networks: a theoretical framework for Universal Approximation and training
Umberto Biccari
Spiking Neural Networks (SNNs) are widely regarded as a biologically-inspired and energy-efficient alternative to classical artificial neural networks. Yet, their theoretical found…
A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints
Roberto Morales, Umberto Biccari
This article introduces a generalized framework for Decentralized Learning formulated as a Multi-Objective Optimization problem, in which both distributed agents and a central coor…