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ORCIDAlberto Carlevaro
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2020 – today
- 2024
[b1]Alberto Carlevaro:
Mathematical Methods for eXplainable and Reliable Machine Learning in Trustworthy Artificial Intelligence. University of Genoa, Italy, 2024- [j6]Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene, Maurizio Mongelli:
Conformal predictions for probabilistically robust scalable machine learning classification. Mach. Learn. 113(9): 6645-6661 (2024) - [j5]Alberto Carlevaro
, Marta Lenatti, Alessia Paglialonga, Maurizio Mongelli:
Multiclass Counterfactual Explanations Using Support Vector Data Description. IEEE Trans. Artif. Intell. 5(6): 3046-3056 (2024) - [c5]Sara Narteni, Alberto Carlevaro, Jérôme Guzzi, Maurizio Mongelli:
Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions. xAI (4) 2024: 396-417 - [i3]Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene, Maurizio Mongelli:
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification. CoRR abs/2403.10368 (2024) - 2023
- [j4]Alberto Sassu, Jacopo Motta, Alessandro Deidda, Luca Ghiani, Alberto Carlevaro, Giovanni Garibotto, Filippo Gambella:
Artichoke deep learning detection network for site-specific agrochemicals UAS spraying. Comput. Electron. Agric. 213: 108185 (2023) - [c4]Sara Narteni, Alberto Carlevaro, Fabrizio Dabbene, Marco Muselli, Maurizio Mongelli:
CONFIDERAI: CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence. COPA 2023: 485-487 - [i2]Alberto Carlevaro, Sara Narteni, Fabrizio Dabbene, Marco Muselli, Maurizio Mongelli:
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence. CoRR abs/2309.01778 (2023) - [i1]Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene, Maurizio Mongelli:
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers. CoRR abs/2309.04627 (2023) - 2022
- [j3]Alberto Carlevaro, Marta Lenatti, Alessia Paglialonga, Maurizio Mongelli:
Counterfactual Building and Evaluation via eXplainable Support Vector Data Description. IEEE Access 10: 60849-60861 (2022) - [j2]Ivan Vaccari, Alberto Carlevaro, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli:
eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics. IEEE Access 10: 83949-83970 (2022) - [j1]Alberto Carlevaro, Maurizio Mongelli:
A New SVDD Approach to Reliable and Explainable AI. IEEE Intell. Syst. 37(2): 55-68 (2022) - [c3]Ivan Vaccari, Alberto Carlevaro, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli:
On The Detection Of Adversarial Attacks Through Reliable AI. INFOCOM Workshops 2022: 1-6 - [c2]Marta Lenatti, Alberto Carlevaro, Karim Keshavjee, Aziz Guergachi, Alessia Paglialonga, Maurizio Mongelli:
Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI. MIE 2022: 98-103 - 2021
- [c1]Alberto Carlevaro, Maurizio Mongelli:
Reliable AI Through SVDD and Rule Extraction. CD-MAKE 2021: 153-171
Coauthor Index
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