Robert A Vandermeulen
Title
Cited by
Cited by
Year
Deep One-Class Classification
L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ...
International Conference on Machine Learning, 4390-4399, 2018
6422018
Deep semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
arXiv preprint arXiv:1906.02694, 2019
1502019
Image anomaly detection with generative adversarial networks
L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft
Joint european conference on machine learning and knowledge discovery in …, 2018
1272018
A unifying review of deep and shallow anomaly detection
L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ...
Proceedings of the IEEE, 2021
1022021
Explainable deep one-class classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
arXiv preprint arXiv:2007.01760, 2020
362020
Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text
L Ruff, Y Zemlyanskiy, R Vandermeulen, T Schnake, M Kloft
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
242019
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion
F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ...
The journal of physical chemistry letters 11 (3), 981-985, 2020
222020
Consistency of robust kernel density estimators
R Vandermeulen, C Scott
Conference on Learning Theory, 568-591, 2013
212013
Rethinking assumptions in deep anomaly detection
L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft
ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021
202021
Deep support vector data description for unsupervised and semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Gornitz, A Binder, E Muller, M Kloft
Proceedings of the ICML 2019 Workshop on Uncertainty and Robustness in Deep …, 2019
102019
An Operator Theoretic Approach to Nonparametric Mixture Models
RA Vandermeulen, CD Scott
Annals of Statistics 47 (5), 2704-2733, 2019
92019
On the identifiability of mixture models from grouped samples
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1502.06644, 2015
92015
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
A Ritchie, RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 33, 2020
62020
Robust kernel density estimation by scaling and projection in Hilbert space
RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 27, 433-441, 2014
62014
Deep Anomaly Detection by Residual Adaptation
L Deecke, L Ruff, RA Vandermeulen, H Bilen
4*
A Proposal for Supervised Density Estimation
RA Vandermeulen, R Saitenmacher, A Ritchie
NeurIPS Pre-Registration Workshop, 2020
32020
Transfer-based semantic anomaly detection
L Deecke, L Ruff, RA Vandermeulen, H Bilen
International Conference on Machine Learning, 2546-2558, 2021
12021
Improving nonparametric density estimation with tensor decompositions
RA Vandermeulen
arXiv preprint arXiv:2010.02425, 2020
12020
Supplement to “An operator theoretic approach to nonparametric mixture models.”
RA Vandermeulen, CD Scott
DOI, 2019
12019
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
RA Vandermeulen, A Ledent
Advances in Neural Information Processing Systems 34, 2021
2021
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