Seuraa
Shubhomoy Das
Shubhomoy Das
Tuntematon yhteys
Vahvistettu sähköpostiosoite verkkotunnuksessa espressive.com - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
1752016
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
1752013
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
1752013
A meta-analysis of the anomaly detection problem
A Emmott, S Das, T Dietterich, A Fern, WK Wong
arXiv preprint arXiv:1503.01158, 2015
1022015
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
632013
Incorporating feedback into tree-based anomaly detection
S Das, WK Wong, A Fern, TG Dietterich, MA Siddiqui
arXiv preprint arXiv:1708.09441, 2017
622017
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
352021
Active anomaly detection via ensembles: Insights, algorithms, and interpretability
S Das, MR Islam, NK Jayakodi, JR Doppa
arXiv preprint arXiv:1901.08930, 2019
302019
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
292013
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
172020
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
172011
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
162011
Finite Sample Complexity of Rare Pattern Anomaly Detection.
MA Siddiqui, A Fern, TG Dietterich, S Das
UAI 16, 686-695, 2016
142016
Active anomaly detection via ensembles
S Das, MR Islam, NK Jayakodi, JR Doppa
arXiv preprint arXiv:1809.06477, 2018
122018
Anomaly detection meta-analysis benchmarks
A Emmott, S Das, T Dietterich, A Fern, WK Wong
52016
Glad: Glocalized anomaly detection via human-in-the-loop learning
MR Islam, S Das, JR Doppa, S Natarajan
arXiv preprint arXiv:1810.01403, 2018
42018
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
4*2018
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
12011
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
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Artikkelit 1–20