Perini, Lorenzo

12 publications

TMLR 2026 Dealing with Uncertainty in Contextual Anomaly Detection Luca Bindini, Lorenzo Perini, Stefano Nistri, Jesse Davis, Paolo Frasconi

TMLR 2025 Uncertainty-Aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu

DMLR 2024 Deep Neural Network Benchmarks for Selective Classification Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri

MLJ 2024 Machine Learning with a Reject Option: A Survey Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis

ECML-PKDD 2023 Detecting Evasion Attacks in Deployed Tree Ensembles Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis

ICML 2023 Estimating the Contamination Factor’s Distribution in Unsupervised Anomaly Detection Lorenzo Perini, Paul-Christian Bürkner, Arto Klami

ECML-PKDD 2023 Semi-Supervised Learning from Active Noisy Soft Labels for Anomaly Detection Timo Martens, Lorenzo Perini, Jesse Davis

NeurIPS 2023 Unsupervised Anomaly Detection with Rejection Lorenzo Perini, Jesse Davis

ECML-PKDD 2022 Multi-Domain Active Learning for Semi-Supervised Anomaly Detection Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis

AAAI 2022 Transferring the Contamination Factor Between Anomaly Detection Domains by Shape Similarity Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

IJCAI 2020 Class Prior Estimation in Active Positive and Unlabeled Learning Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

ECML-PKDD 2020 Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions Lorenzo Perini, Vincent Vercruyssen, Jesse Davis