Feature partitioning for robust tree ensembles and their certification in adversarial scenarios. - Stefano Calzavara; Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando 001 (EURASIP J. Inf. Secur., 2021)

Filtering out Outliers in Learning to Rank. - Federico Marcuzzi; Claudio Lucchese; Salvatore Orlando 001 (ICTIR, 2022)

SOUR an Outliers Detection Algorithm in Learning to Rank (Abstract). - Federico Marcuzzi; Claudio Lucchese; Salvatore Orlando 001 (IIR, 2022)

Beyond robustness: Resilience verification of tree-based classifiers. - Stefano Calzavara; Lorenzo Cazzaro; Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando 001 (Comput. Secur., 2022)

LambdaRank Gradients are Incoherent. - Federico Marcuzzi; Claudio Lucchese; Salvatore Orlando 001 (CIKM, 2023)

Does LambdaMART Do What You Expect? - Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando 001 (IIR, 2023)

On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting. - Claudio Lucchese; Federico Marcuzzi; Salvatore Orlando 001 (SAC, 2023)

Explainable Global Fairness Verification of Tree-Based Classifiers. - Stefano Calzavara; Lorenzo Cazzaro; Claudio Lucchese; Federico Marcuzzi (SaTML, 2023)

LambdaFair: a Fair and Effective LambdaMART. - Federico Marcuzzi; Claudio Lucchese; Salvatore Orlando 001 (IIR, 2024)

Software Systems Compliance with the AI Act: Lessons Learned from an International Challenge. - Teresa Scantamburlo; Paolo Falcarin; Alberto Veneri; Alessandro Fabris; Chiara Gallese; Valentina Billa; Francesca Rotolo; Federico Marcuzzi (RAIE@ICSE, 2024)

Effective, Efficient, and Robust Learning Algorithms for Ranking and Classification. - Federico Marcuzzi (SIGIR Forum, 2024)

LambdaFair for Fair and Effective Ranking. - Federico Marcuzzi; Claudio Lucchese; Salvatore Orlando 001 (ECIR (4), 2025)