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Lorenzo Perini
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2020 – today
- 2024
- [j2]Giovanna Sozzi
, Giovanni Chiorboli
Investigating Mesa Structure Impact on C-V Measurements. IEEE Access 12: 32938-32943 (2024) - [j1]Kilian Hendrickx
Machine learning with a reject option: a survey. Mach. Learn. 113(5): 3073-3110 (2024) - [c12]Luca Stradiotti, Lorenzo Perini, Jesse Davis:
Combining Active Learning and Learning to Reject for Anomaly Detection. ECAI 2024: 2266-2273 - [c11]Luca Stradiotti
Semi-Supervised Isolation Forest for Anomaly Detection. SDM 2024: 670-678 - [i6]Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri:
Deep Neural Network Benchmarks for Selective Classification. CoRR abs/2401.12708 (2024) - [i5]Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu:
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. CoRR abs/2405.13699 (2024) - 2023
- [c10]Lorenzo Perini, Paul-Christian Bürkner, Arto Klami:
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. ICML 2023: 27668-27679 - [c9]Lorenzo Perini
Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection. KDD 2023: 1897-1906 - [c8]Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. NeurIPS 2023 - [c7]Laurens Devos
Detecting Evasion Attacks in Deployed Tree Ensembles. ECML/PKDD (5) 2023: 120-136 - [c6]Timo Martens
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. ECML/PKDD (1) 2023: 219-236 - [i4]Lorenzo Perini, Daniele Giannuzzi, Jesse Davis
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection. CoRR abs/2301.02909 (2023) - [i3]Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. CoRR abs/2305.13189 (2023) - 2022
- [c5]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity. AAAI 2022: 4128-4136 - [c4]Vincent Vercruyssen
Multi-domain Active Learning for Semi-supervised Anomaly Detection. ECML/PKDD (4) 2022: 485-501 - [i2]Lorenzo Perini, Paul C. Bürkner, Arto Klami:
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. CoRR abs/2210.10487 (2022) - 2021
- [i1]Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis:
Machine Learning with a Reject Option: A survey. CoRR abs/2107.11277 (2021) - 2020
- [c3]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis
Class Prior Estimation in Active Positive and Unlabeled Learning. IJCAI 2020: 2915-2921 - [c2]Lorenzo Perini
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. ECML/PKDD (3) 2020: 227-243 - [c1]Lorenzo Perini
A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods. PKDD/ECML Workshops 2020: 397-408
Coauthor Index
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last updated on 2024-10-28 20:14 CET by the dblp team
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