Lorenzo Perini

Latest

• Operational, Uncertainty-Aware, and Reliable Anomaly Detection
• Combining Active Learning and Learning to Reject for Anomaly Detection
• Deep neural network benchmarks for selective classification
• Machine learning with a reject option: A survey
• Semi-Supervised Isolation Forest for Anomaly Detection
• Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
• Unsupervised anomaly detection with rejection
• Unsupervised Anomaly Detection with Rejection @ NeurIPS23
• Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection @ KDD23
• Estimating the Contamination Factor’s Distribution in Unsupervised Anomaly Detection @ ICML23
• Detecting evasion attacks in deployed tree ensembles
• Estimating the contamination factor’s distribution in unsupervised anomaly detection
• How to allocate your label budget? choosing between active learning and learning to reject in anomaly detection
• Learning from positive and unlabeled multi-instance bags in anomaly detection
• Semi-supervised learning from active noisy soft labels for anomaly detection
• Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity @ AAAI22
• Multi-domain active learning for semi-supervised anomaly detection
• Transferring the contamination factor between anomaly detection domains by shape similarity
• Class Prior Estimation in Active Positive and Unlabeled Learning @ IJCAI20
• The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methods
• Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions @ ECML20
• A ranking stability measure for quantifying the robustness of anomaly detection methods
• Class Prior Estimation in Active Positive and Unlabeled Learning.
• Quantifying the confidence of anomaly detectors in their example-wise predictions
• Predictive Maintenance for off-road vehicles based on Hidden Markov Models and Autoencoders for trend Anomaly Detection