Class Prior Estimation in Active Positive and Unlabeled Learning. - Lorenzo Perini; Vincent Vercruyssen; Jesse Davis (IJCAI, 2020)

A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods. - Lorenzo Perini; Connor Galvin; Vincent Vercruyssen (PKDD/ECML Workshops, 2020)

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. - Lorenzo Perini; Vincent Vercruyssen; Jesse Davis (ECML/PKDD (3), 2020)

Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity. - Lorenzo Perini; Vincent Vercruyssen; Jesse Davis (AAAI, 2022)

Multi-domain Active Learning for Semi-supervised Anomaly Detection. - Vincent Vercruyssen; Lorenzo Perini; Wannes Meert; Jesse Davis (ECML/PKDD (4), 2022)

Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. - Lorenzo Perini; Paul-Christian Bürkner; Arto Klami (ICML, 2023)

Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection. - Lorenzo Perini; Vincent Vercruyssen; Jesse Davis (KDD, 2023)

Unsupervised Anomaly Detection with Rejection. - Lorenzo Perini; Jesse Davis (NeurIPS, 2023)

Detecting Evasion Attacks in Deployed Tree Ensembles. - Laurens Devos; Lorenzo Perini; Wannes Meert; Jesse Davis (ECML/PKDD (5), 2023)

Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. - Timo Martens; Lorenzo Perini; Jesse Davis (ECML/PKDD (1), 2023)

How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection. - Lorenzo Perini; Daniele Giannuzzi; Jesse Davis (CoRR, 2023)

Combining Active Learning and Learning to Reject for Anomaly Detection. - Luca Stradiotti; Lorenzo Perini; Jesse Davis (ECAI, 2024)

Semi-Supervised Isolation Forest for Anomaly Detection. - Luca Stradiotti; Lorenzo Perini; Jesse Davis (SDM, 2024)

Investigating Mesa Structure Impact on C-V Measurements. - Giovanna Sozzi; Giovanni Chiorboli; Lorenzo Perini; Roberta Nipoti (IEEE Access, 2024)

Deep Neural Network Benchmarks for Selective Classification. - Andrea Pugnana; Lorenzo Perini; Jesse Davis; Salvatore Ruggieri (CoRR, 2024)

Machine learning with a reject option: a survey. - Kilian Hendrickx; Lorenzo Perini; Dries Van der Plas; Wannes Meert; Jesse Davis (Mach. Learn., 2024)

Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. - Lorenzo Perini; Maja Rudolph; Sabrina Schmedding; Chen Qiu 001 (Trans. Mach. Learn. Res., 2025)

Measuring multi-calibration. - Ido Guy; Daniel Haimovich; Fridolin Linder; Nastaran Okati; Lorenzo Perini; Niek Tax; Mark Tygert (CoRR, 2025)

Dealing with Uncertainty in Contextual Anomaly Detection. - Luca Bindini; Lorenzo Perini; Stefano Nistri; Jesse Davis; Paolo Frasconi (CoRR, 2025)