| Incorporating expert feedback into active anomaly discovery S Das, WK Wong, T Dietterich, A Fern, A Emmott 2016 IEEE 16th International Conference on Data Mining (ICDM), 853-858, 2016 | 191 | 2016 |
| Systematic construction of anomaly detection benchmarks from real data AF Emmott, S Das, T Dietterich, A Fern, WK Wong Proceedings of the ACM SIGKDD workshop on outlier detection and description …, 2013 | 184 | 2013 |
| Detecting insider threats in a real corporate database of computer usage activity TE Senator, HG Goldberg, A Memory, WT Young, B Rees, R Pierce, ... Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 180 | 2013 |
| A meta-analysis of the anomaly detection problem A Emmott, S Das, T Dietterich, A Fern, WK Wong arXiv preprint arXiv:1503.01158, 2015 | 111 | 2015 |
| Incorporating feedback into tree-based anomaly detection S Das, WK Wong, A Fern, TG Dietterich, MA Siddiqui arXiv preprint arXiv:1708.09441, 2017 | 74 | 2017 |
| You are the only possible oracle: Effective test selection for end users of interactive machine learning systems A Groce, T Kulesza, C Zhang, S Shamasunder, M Burnett, WK Wong, ... IEEE Transactions on Software Engineering 40 (3), 307-323, 2013 | 70 | 2013 |
| Active anomaly detection for time-domain discoveries EEO Ishida, MV Kornilov, KL Malanchev, MV Pruzhinskaya, AA Volnova, ... Astronomy & Astrophysics (A&A) 650 (ISSN: 0004-6361 ; e-ISSN: 1432-0746), 2021 | 46 | 2021 |
| Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning S Das, MR Islam, NK Jayakodi, JR Doppa Journal of Artificial Intelligence Research 80 (2024), 127-170, 2024 | 33* | 2024 |
| End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression S Das, T Moore, WK Wong, S Stumpf, I Oberst, K McIntosh, M Burnett Artificial Intelligence 204, 56-74, 2013 | 31 | 2013 |
| Discovering anomalies by incorporating feedback from an expert S Das, WK Wong, T Dietterich, A Fern, A Emmott ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (4), 1-32, 2020 | 19 | 2020 |
| Where are my intelligent assistant’s mistakes? A systematic testing approach T Kulesza, M Burnett, S Stumpf, WK Wong, S Das, A Groce, A Shinsel, ... End-User Development: Third International Symposium, IS-EUD 2011, Torre …, 2011 | 17 | 2011 |
| End-user feature labeling: A locally-weighted regression approach WK Wong, I Oberst, S Das, T Moore, S Stumpf, K McIntosh, M Burnett Proceedings of the 16th international conference on Intelligent user …, 2011 | 16 | 2011 |
| Finite Sample Complexity of Rare Pattern Anomaly Detection. MA Siddiqui, A Fern, TG Dietterich, S Das UAI 16, 686-695, 2016 | 15 | 2016 |
| Active anomaly detection via ensembles S Das, MR Islam, NK Jayakodi, JR Doppa arXiv preprint arXiv:1809.06477, 2018 | 12 | 2018 |
| GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning M Rakibul Islam, S Das, J Rao Doppa, S Natarajan arXiv e-prints, arXiv: 1810.01403, 2018 | 8* | 2018 |
| Anomaly detection meta-analysis benchmarks A Emmott, S Das, T Dietterich, A Fern, WK Wong | 6 | 2016 |
| End-user feature labeling via locally weighted logistic regression WK Wong, I Oberst, S Das, T Moore, S Stumpf, K McIntosh, M Burnett Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1575-1578, 2011 | 1 | 2011 |
| Incorporating User Feedback into Machine Learning Systems S Das | | 2017 |
| End-User Feature Labeling: Supervised and Semi-supervised Approaches Based on Locally-Weighted Logistic Regression K McIntosh, S Das, S Stumpf, T Moore, M Burnetta, WK Wong, I Oberst | | 2013 |
Weng-Keen WongProfessor of Computer Science, Oregon State UniversityVerified email at eecs.oregonstate.edu