Lorenzo Perini

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 18

Top Topics

Unlabeled Instances

Anomaly Detection

Active Learning

Shape Similarity

Top Venues

Publications

Luca Stradiotti, Lorenzo Perini, Jesse Davis
Semi-Supervised Isolation Forest for Anomaly Detection. SDM (2024)
Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri
Deep Neural Network Benchmarks for Selective Classification. CoRR (2024)
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis
Machine learning with a reject option: a survey. Mach. Learn. 113 (5) (2024)
Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. CoRR (2024)
Lorenzo Perini, Jesse Davis
Unsupervised Anomaly Detection with Rejection. CoRR (2023)
Lorenzo Perini, Jesse Davis
Unsupervised Anomaly Detection with Rejection. NeurIPS (2023)
Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection. KDD (2023)
Timo Martens, Lorenzo Perini, Jesse Davis
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. ECML/PKDD (1) (2023)
Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis
Detecting Evasion Attacks in Deployed Tree Ensembles. ECML/PKDD (5) (2023)
Lorenzo Perini, Paul-Christian Bürkner, Arto Klami
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. ICML (2023)
Lorenzo Perini, Daniele Giannuzzi, Jesse Davis
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection. CoRR (2023)
Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity. AAAI (2022)
Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis
Multi-domain Active Learning for Semi-supervised Anomaly Detection. ECML/PKDD (4) (2022)
Lorenzo Perini, Paul C. Bürkner, Arto Klami
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. CoRR (2022)
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis
Machine Learning with a Reject Option: A survey. CoRR (2021)
Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. ECML/PKDD (3) (2020)
Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Class Prior Estimation in Active Positive and Unlabeled Learning. IJCAI (2020)
Lorenzo Perini, Connor Galvin, Vincent Vercruyssen
A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods. PKDD/ECML Workshops (2020)