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Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Conformal predictions make it possible to define reliable and robust learning algorithms.
One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
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
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
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.
3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy
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
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed.
