Publications

Unsupervised anomaly detection with rejection

Lorenzo Perini, Jesse Davis

Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior

Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu

Semi-Supervised Isolation Forest for Anomaly Detection

Luca Stradiotti, Lorenzo Perini, Jesse Davis

Machine learning with a reject option: A survey

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

Deep neural network benchmarks for selective classification

Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri

Combining Active Learning and Learning to Reject for Anomaly Detection

Luca Stradiotti, Lorenzo Perini, Jesse Davis

Semi-supervised learning from active noisy soft labels for anomaly detection

Timo Martens, Lorenzo Perini, Jesse Davis

Learning from positive and unlabeled multi-instance bags in anomaly detection

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

How to allocate your label budget? choosing between active learning and learning to reject in anomaly detection

Lorenzo Perini, Daniele Giannuzzi, Jesse Davis

Estimating the contamination factor’s distribution in unsupervised anomaly detection

Lorenzo Perini, Paul-Christian Bürkner, Arto Klami

Detecting evasion attacks in deployed tree ensembles

Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis

Transferring the contamination factor between anomaly detection domains by shape similarity

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Multi-domain active learning for semi-supervised anomaly detection

Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis

The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methods

Jonas Soenen, Elia Van Wolputte, Lorenzo Perini, Vincent Vercruyssen, Wannes Meert, Jesse Davis, Hendrik Blockeel

Quantifying the confidence of anomaly detectors in their example-wise predictions

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

Class Prior Estimation in Active Positive and Unlabeled Learning.

Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

A ranking stability measure for quantifying the robustness of anomaly detection methods

Lorenzo Perini, Connor Galvin, Vincent Vercruyssen

Predictive Maintenance for off-road vehicles based on Hidden Markov Models and Autoencoders for trend Anomaly Detection

Lorenzo Perini