DISSERTATION ABSTRACT

Effective, Efficient, and Robust Learning
Algorithms for Ranking and Classification
Federico Marcuzzi
Università Ca’ Foscari di Venezia
Italy
federico.marcuzzi@unive.itAbstract

Over the past decade, machine learning has gained significant traction and is now deployed acrossdiverse domains, including information systems, finance, healthcare, cybersecurity, autonomous
driving, and more. As machine learning finds applications in various sensitive scenarios, the demand for models that exhibit accuracy and robustness during the operational phase has grownexponentially. One crucial factor that profoundly shapes the quality of machine learning models
revolves around the training data they rely upon and the input data encountered at the operational phase. Therefore, the development of data-aware algorithms is of paramount importancein achieving high-quality machine-learning models. This thesis contributes to this overarching
objective by delving into the development of data-aware algorithms, emphasizing the importanceof this awareness during both the training and operational phases of machine learning models.
The research presented in this thesis focuses on two primary domains. The first domain is information retrieval, with a particular emphasis on enhancing both the efficiency of learning-to-ranklearning algorithms and the effectiveness of the learned models in solving ranking tasks. The
thesis includes three works in this domain: Marcuzzi et al. [2022] provides a novel algorithm
to detect and remove consistent-outliers documents from the training data. In Marcuzzi et al.
[2023], we designed a new learning algorithm that handles the problem of gradient incoherencies
affecting LambdaRank-based algorithms. Finally, in Lucchese et al. [2023], we designed a newsampling function for the Selective Gradient Boosting algorithm to exploit the most useful lowranked non-relevant document. The second domain is adversarial machine learning, which focuseson increasing the robustness of binary classifiers against adversarial inputs encountered at the operational phase. Furthermore, the research in this domain focuses on providing certifiable models
to efficiently assess robustness against adversarial machine learning attacks. In this regard, inCalzavara et al. [2021], we designed a novel robust learning algorithm to train ensembles of decision trees robust to evasion attacks along with its polynomial robustness-certification algorithm
designed to compute a robustness lower bound. Finally, in Calzavara et al. [2022], we provided a
new evaluation metric named Resilience to better access the security of machine learning models.
Awarded by: Università Ca’ Foscari di Venezia, Venice, Italy on 19 April 2024.
Supervised by: Claudio Lucchese.
Available at: https://federicomarcuzzi.github.io/resources/thesis_phd.pdf.
ACM SIGIR Forum1Vol. 58 No. 1 – June 2024

Selected Publications
Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi, and Salvatore Orlando. Feature partitioning for robust tree ensembles and their certification in adversarial scenarios. EURASIPJ. Inf. Secur., 2021(1):12, 2021. doi: 10.1186/S13635- 021- 00127-0. URL https://doi.org/10.1186/s13635-021-00127-0.
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Federico Marcuzzi, and Salvatore Orlando.
Beyond robustness: Resilience verification of tree-based classifiers. Comput. Secur., 121:102843,2022. doi: 10.1016/J.COSE.2022.102843. URL https://doi.org/10.1016/j.cose.2022.102843.
Claudio Lucchese, Federico Marcuzzi, and Salvatore Orlando. On the effect of low-ranked documents: A new sampling function for selective gradient boosting. In Jiman Hong, MaartLanperne, Juw Won Park, Tomás Cerný, and Hossain Shahriar, editors, Proceedings of the
38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March27-31, 2023, pages 646–652. ACM, 2023. doi: 10.1145/3555776.3577597. URL https://doi.org/10.1145/3555776.3577597.
Federico Marcuzzi, Claudio Lucchese, and Salvatore Orlando. Filtering out outliers in learningto rank. In Fabio Crestani, Gabriella Pasi, and Éric Gaussier, editors, ICTIR ’22: The 2022
ACM SIGIR International Conference on the Theory of Information Retrieval, Madrid, Spain,
July 11 - 12, 2022, pages 214–222. ACM, 2022. doi: 10.1145/3539813.3545127. URL https://doi.org/10.1145/3539813.3545127.
Federico Marcuzzi, Claudio Lucchese, and Salvatore Orlando. Lambdarank gradients are incoherent. In Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, MinZhang, and Rodrygo L. T. Santos, editors, Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom,October 21-25, 2023, pages 1777–1786. ACM, 2023. doi: 10.1145/3583780.3614948. URL
https://doi.org/10.1145/3583780.3614948.

ACM SIGIR Forum2Vol. 58 No. 1 – June 2024