Lorenzo Perini

According to our database, Lorenzo Perini authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Deep Neural Network Benchmarks for Selective Classification.

Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri

CoRR, 2024

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

Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection.

Timo Martens, Lorenzo Perini, Jesse Davis

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Detecting Evasion Attacks in Deployed Tree Ensembles.

Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Unsupervised Anomaly Detection with Rejection.

Lorenzo Perini, Jesse Davis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection.

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection.

Lorenzo Perini, Paul-Christian Bürkner, Arto Klami

Proceedings of the International Conference on Machine Learning, 2023

2022

Multi-domain Active Learning for Semi-supervised Anomaly Detection.

Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity.

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Machine Learning with a Reject Option: A survey.

Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis

CoRR, 2021

2020

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions.

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods.

Lorenzo Perini, Connor Galvin, Vincent Vercruyssen

Proceedings of the ECML PKDD 2020 Workshops, 2020

Class Prior Estimation in Active Positive and Unlabeled Learning.

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020