Search Results for author: Fabrizio Dabbene

Found 10 papers, 0 papers with code

Measured-state conditioned recursive feasibility for stochastic model predictive control

no code implementations19 Jun 2024 Mirko Fiacchini, Martina Mammarella, Fabrizio Dabbene

First, motivated by the difficulty of guaranteeing recursive feasibility in this framework, due to the nonzero probability of violating chance-constraints in the case of unbounded noise, we introduce the novel definition of measured-state conditioned recursive feasibility in expectation.

Model Predictive Control

Optimal policy design for decision problems under social influence

no code implementations11 Jun 2024 Valentina Breschi, Chiara Ravazzi, Paolo Frasca, Fabrizio Dabbene, Mara Tanelli

This paper focuses on devising strategies for control-oriented decision-making scenarios, in the presence of social and external influences, e. g. within recommending systems in social contexts.

Decision Making

A novel mathematical model for predicting the benefits of physical activity on type 2 diabetes progression

no code implementations23 Apr 2024 Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene, Karim Keshavjee, Pasquale Palumbo, Alessia Paglialonga

Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature surprisingly lacks validated models able to predict the long-term benefits of exercise on T2D progression and support personalized risk prediction and prevention.

One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION

no code implementations31 Oct 2023 Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa

The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm.

Probabilistic Safety Regions Via Finite Families of Scalable Classifiers

no code implementations8 Sep 2023 Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene, Maurizio Mongelli

The notion of scalable classifiers is then exploited to link the tuning of machine learning with error control.

CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence

no code implementations4 Sep 2023 Sara Narteni, Alberto Carlevaro, Fabrizio Dabbene, Marco Muselli, Maurizio Mongelli

Everyday life is increasingly influenced by artificial intelligence, and there is no question that machine learning algorithms must be designed to be reliable and trustworthy for everyone.

Conformal Prediction Disease Prediction +1

3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy

no code implementations6 Feb 2022 Martina Mammarella, Cesare Donati, Takumi Shimizu, Masaya Suenaga, Lorenzo Comba, Alessandro Biglia, Kuniaki Uto, Takeshi Hatanaka, Paolo Gay, Fabrizio Dabbene

In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle.

Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION

no code implementations15 Jan 2021 Martina Mammarella, Victor Mirasierra, Matthias Lorenzen, Teodoro Alamo, Fabrizio Dabbene

In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed.

Model Predictive Control

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