3 citations · 3 across the 4 of their papers we have counts for
4 papers
Exact characterization of ε-Safe Decision Regions for exponential family distributions and Multi Cost SVM approximation
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene +1
Probabilistic guarantees on the prediction of data-driven classifiers are necessary to define models that can be considered reliable. This is a key requirement for modern machine l…
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene +1
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be…
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene +1
Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerica…
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence
Sara Narteni, Alberto Carlevaro, Fabrizio Dabbene +2
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 e…