Francesco Busolin, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani. Early Exit Strategies for Learning-to-Rank Cascades. IEEE Access.
Claudio Lucchese, Giorgia Minello, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri. Can Embeddings Analysis Explain Large Language Model Ranking? CIKM 2023
Federico Marcuzzi, Claudio Lucchese, Salvatore Orlando. LambdaRank Gradients are Incoherent. CIKM 2023.
Matteo Rizzo, Alberto Veneri, Andrea Albarelli, Claudio Lucchese, Marco Salvatore Nobile, Cristina Conati. A Theoretical Framework for AI Models Explainability with Application in Biomedicine. CIBCB 2023.
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini. Efficient and Effective Tree-based and Neural Learning to Rank. Foundations and Trends® in Information Retrieval.
Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando. On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting. ACM/SIGAPP Symposium on Applied Computing 2023.
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Federico Marcuzzi. Explainable Global Fairness Verification of Tree-Based Classifiers. SaTML 2023.
Claudio Lucchese, Salvatore Orlando, Raffaele Perego, Alberto Veneri. GAM Forest Explanation. EDBT 2023.
Federico Marcuzzi, Claudio Lucchese, Salvatore Orlando. Filtering out Outliers in Learning to Rank. ICTIR 2022.
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando. Beyond robustness: Resilience verification of tree-based classifiers. Computer and Security 2022.
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri. ILMART: Interpretable Ranking with Constrained LambdaMART. SIGIR 2022.
Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando. Feature partitioning for robust tree ensembles and their certification in adversarial scenarios. EURASIP J. Inf. Secur. 2021(1): 12 (2021)
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese: AMEBA: An Adaptive Approach to the Black-Box Evasion of Machine Learning Models. AsiaCCS 2021: 292-306
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani: Learning Early Exit Strategies for Additive Ranking Ensembles. SIGIR 2021: 2217-2221
Calzavara, S., Lucchese, C., Tolomei, G. et al. TREANT: training evasion-aware decision trees. Data Mining and Knowledge Discovery 2020
Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego: Boosting learning to rank with user dynamics and continuation methods. Inf. Retr. J. 23(6): 528-554 (2020)
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani. Query-level Early Exit for Additive Learning-to-Rank Ensembles. SIGIR 2020, pp. 2033-2036
Calzavara S., Lucchese C., Ferrara P. Certifying decision trees against evasion attacks by program analysis, European Symposium on Research in Computer Security (ESORICS), 2020.
Calzavara, S., Lucchese, C., Tolomei G. Adversarial Training of Gradient-Boosted Decision Trees. In CIKM ’19: Proceedings of the The 27th ACM International Conference on Information and Knowledge Management (2019). (Short), (acceptance 21.3%).
Calzavara, S., Conti, M., Focardi, R., Rabitti, A., Tolomei, G. Mitch: A machine learning approach to the black-box detection of CSRF vulnerabilities. In EuroS&P '19: Proceedings of the 4th IEEE European Symposium on Security and Privacy (2019). (acceptance 20.0%)
Lucchese, C., Nardini, F. M., Pasumarthi, R. K., Bruch, S., Bendersky, M., Wang, X., Oosterhuis, H., Jagerman, R.,and de Rijke, M. Learning to rank in theory and practice: From gradient boosting to neural networks and unbiased learning. In SIGIR ’19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019), ACM, pp. 1419–1420. (Tutorial).
Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Trani, S. Selective gradient boosting for effective learning to rank. In SIGIR ’18: Proceedings of the 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (2018). (acceptance 21%).
Lulli, A., Carlini, E., Dazzi, P., Lucchese, C., And Ricci, L. Fast connected components computation in large graphs by vertex pruning. IEEE TPDS Transactions on Parallel and Distributed Systems, (2016).
Calzavara, S., Tolomei, G., Casini, A., Bugliesi, M., Orlando, S. A supervised learning approach to protect client authentication on the Web. ACM TWEB Transactions on the Web, 2015.
Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Tonellotto, N., And Venturini, R. Quickscorer: a fast algorithm to rank documents with additive ensembles of regression trees. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015). (Best Paper) (ACM Notable Article). (acceptance 20%).
Lucchese, C., Orlando, S., And Perego, R. A unifying framework for mining approximate top-k binary patterns. IEEE TKDE Transactions On Knowledge and Data Engineering 26, 12 (2014), 2900–2913.
Lucchese, C., Orlando, S., Perego, R., Silvestri, F., And Tolomei, G. Discovering tasks from search engine query logs. ACM TOIS Transactions on Information Systems 31, 3 (2013). (ACM Notable Article).
Boley, M., Lucchese, C., Paurat, D., And Gartner, T. Direct local pattern sampling by efficient two-step random procedures. In KDD ’11: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011), pp. 582–590. (acceptance 7.8%).
Lucchese, C., Rayan, D., Vlachos, M., And Yu, P. S. Rights protection of trajectory datasets with nearest-neighbor preservation. VLDB Journal 19, 4 (2010), 531–556.
Lucchese, C., Orlando, S., And Perego, R. Fast and memory efficient mining of frequent closed itemsets. IEEE TKDE Transactions On Knowledge and Data Engineering 18, 1 (2006), 21–36.