| Semi-supervised Anomaly Detection with an Application to Water Analytics V Vercruyssen, W Meert, G Verbruggen, K Maes, R Bäumer, J Davis IEEE International Conference on Data Mining (ICDM), 527-536, 2018 | 106 | 2018 |
| Pattern-based anomaly detection in mixed-type time series L Feremans, V Vercruyssen, B Cule, W Meert, B Goethals Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 60 | 2020 |
| Transfer learning for time series anomaly detection V Vercruyssen, W Meert, J Davis ECML/PKDD Workshop on Interactive Adaptive Learning, 2017 | 41 | 2017 |
| Transfer learning for anomaly detection through localized and unsupervised instance selection V Vincent, M Wannes, D Jesse Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6054-6061, 2020 | 38 | 2020 |
| Qualitative spatial reasoning for soccer pass prediction V Vercruyssen, L De Raedt, J Davis ECML/PKDD Workshop on Machine Learning and Data Mining for Sports Analytics, 2016 | 33 | 2016 |
| 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 | 32 | 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, 1-9, 2021 | 28 | 2021 |
| 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 | 18 | 2022 |
| Class Prior Estimation in Active Positive and Unlabeled Learning. L Perini, V Vercruyssen, J Davis IJCAI, 2915-2921, 2020 | 16 | 2020 |
| 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 | 8 | 2022 |
| 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 | 8 | 2020 |
| A framework for pattern mining and anomaly detection in multi-dimensional time series and event logs L Feremans, V Vercruyssen, W Meert, B Cule, B Goethals ECML/PKDD, 2019 | 6 | 2019 |
| “Now you see it, now you don't!” Detecting Suspicious Pattern Absences in Continuous Time Series V Vercruyssen, W Meert, J Davis Proceedings of the 2020 SIAM international conference on data mining, 127-135, 2020 | 5 | 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 | 4 | 2023 |
| Systematic evaluation of cash search strategies for unsupervised anomaly detection I Antoniadis, V Vercruyssen, J Davis Fourth International Workshop on Learning with Imbalanced Domains: Theory …, 2022 | 3 | 2022 |
| Why are you weird? infusing interpretability in isolation forest for anomaly detection NS Kartha, C Gautrais, V Vercruyssen arXiv preprint arXiv:2112.06858, 2021 | 3 | 2021 |
| AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection J Soenen, E Van Wolputte, V Vercruyssen, W Meert, H Blockeel arXiv preprint arXiv:2305.12958, 2023 | | 2023 |
| Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection N Sobha Kartha, C Gautrais, V Vercruyssen arXiv e-prints, arXiv: 2112.06858, 2021 | | 2021 |
| Designing Anomaly Detection Algorithms that Exploit Flexible Supervision V Vercruyssen | | 2020 |
| Systeemdynamisch gedrag van gedistribueerde elektriciteitsopwekking V Vercruyssen Universiteit Antwerpen, 2014 | | 2014 |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
