Alberto VENERI

Position
Subject expert
E-mail
alberto.veneri@unive.it
860028@stud.unive.it
Website
www.unive.it/people/alberto.veneri (personal record)

Lucchese C.; Nardini F.M.; Orlando S.; Perego R.; Veneri A. Explainable, Effective, and Efficient Learning-to-Rank Models Using ILMART in ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 43, pp. 1-37 (ISSN 1046-8188)
DOI 2025, Journal Article - ARCA card: 10278/5112008


Matteo Rizzo, Alberto Veneri, Matteo Marcuzzo, Alessandro Zangari, Andrea Albarelli, Claudio Lucchese, Marco Salvatore Nobile, Cristina Conati Machine Learning Models Explanations As Interpretations of Evidence: A Theoretical Framework of Explainability and Its Implications on High-Stakes Biomedical Decision-Making in BMC MEDICAL RESEARCH METHODOLOGY, vol. 24 (ISSN 1471-2288)
2025, Journal Article - ARCA card: 10278/5106607


Busolin F.; Lucchese C.; Nardini F.M.; Orlando S.; Perego R.; Trani S.; Veneri A. Efficient Re-ranking with Cross-encoders via Early Exit , SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES, Association for Computing Machinery, Inc, pp. 2534-2544, Convegno: 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, 2025
DOI 2025, Article in Conference Proceedings - ARCA card: 10278/5112007


Albarelli A.; Lucchese C.; Rizzo M.; Veneri A. On the Application of a Common Theoretical Explainability Framework in Information Retrieval , CEUR Workshop Proceedings, CEUR-WS, vol. 3802, pp. 43-52, Convegno: 14th Italian Information Retrieval Workshop, IIR 2024, 2024 (ISSN 1613-0073)
2024, Article in Conference Proceedings - ARCA card: 10278/5084247


Scantamburlo, Teresa; Falcarin, Paolo; Veneri, Alberto; Fabris, Alessandro; Gallese, Chiara; Billa, Valentina; Rotolo, Francesca; Marcuzzi, Federico Software Systems Compliance with the AI Act: Lessons Learned from an International Challenge , Proceedings of the 2nd International Workshop on Responsible AI Engineering, Association for Computing Machinery, pp. 44-51, Convegno: 2024 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE), 24/04/2024 (ISBN 9798400705724)
DOI - URL correlato 2024, Article in Conference Proceedings - ARCA card: 10278/5066021