| Machine learning with a reject option: A survey K Hendrickx, L Perini, D Van der Plas, W Meert, J Davis Machine Learning 113 (5), 3073-3110, 2024 | 138 | 2024 |
| Quantifying the confidence of anomaly detectors in their example-wise predictions L Perini, V Vercruyssen, J Davis Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020 | 35 | 2020 |
| The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methods J Soenen, E Van Wolputte, L Perini, V Vercruyssen, W Meert, J Davis, ... Proceedings of the KDD'21 Workshop on Outlier Detection and Description, 1-9, 2021 | 31 | 2021 |
| Estimating the contamination factor’s distribution in unsupervised anomaly detection L Perini, PC Bürkner, A Klami International Conference on Machine Learning, 27668-27679, 2023 | 24 | 2023 |
| Transferring the contamination factor between anomaly detection domains by shape similarity L Perini, V Vercruyssen, J Davis Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4128-4136, 2022 | 20 | 2022 |
| Class prior estimation in active positive and unlabeled learning L Perini, V Vercruyssen, J Davis Proceedings of the 29th international joint conference on artificial …, 2020 | 16 | 2020 |
| Learning from positive and unlabeled multi-instance bags in anomaly detection L Perini, V Vercruyssen, J Davis Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and …, 2023 | 10 | 2023 |
| A ranking stability measure for quantifying the robustness of anomaly detection methods L Perini, C Galvin, V Vercruyssen ECML PKDD 2020 workshops: Workshops of the european conference on machine …, 2020 | 10 | 2020 |
| Unsupervised anomaly detection with rejection L Perini, J Davis Advances in Neural Information Processing Systems 36, 69673-69691, 2023 | 8 | 2023 |
| Multi-domain active learning for semi-supervised anomaly detection V Vercruyssen, L Perini, W Meert, J Davis Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 7 | 2022 |
| Deep neural network benchmarks for selective classification A Pugnana, L Perini, J Davis, S Ruggieri arXiv preprint arXiv:2401.12708, 2024 | 5 | 2024 |
| Semi-supervised isolation forest for anomaly detection L Stradiotti, L Perini, J Davis Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | 5 | 2024 |
| Detecting evasion attacks in deployed tree ensembles L Devos, L Perini, W Meert, J Davis Joint European conference on machine learning and knowledge discovery in …, 2023 | 4 | 2023 |
| Semi-supervised learning from active noisy soft labels for anomaly detection T Martens, L Perini, J Davis Joint European conference on machine learning and knowledge discovery in …, 2023 | 4 | 2023 |
| Predictive Maintenance for off-road vehicles based on Hidden Markov Models and Autoencoders for trend Anomaly Detection. L Perini Politecnico di Torino, 2019 | 4 | 2019 |
| Combining Active Learning and Learning to Reject for Anomaly Detection L Stradiotti, L Perini, J Davis 27th European Conference on Artificial Intelligence, 19–24 October 2024 …, 2024 | 1 | 2024 |
| Uncertainty-aware evaluation of auxiliary anomalies with the expected anomaly posterior L Perini, M Rudolph, S Schmedding, C Qiu arXiv preprint arXiv:2405.13699, 2024 | 1 | 2024 |
| How to allocate your label budget? choosing between active learning and learning to reject in anomaly detection L Perini, D Giannuzzi, J Davis arXiv preprint arXiv:2301.02909, 2023 | 1 | 2023 |
| Operational, Uncertainty-Aware, and Reliable Anomaly Detection L Perini | | 2024 |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
