Robert A. Vandermeulen

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Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Lukas RuffAignosticsVerified email at aignostics.com
Lucas DeeckeUniversity of EdinburghVerified email at ed.ac.uk
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Nico GoernitzCTO @ MorphAIs | former PostDoc @ TU-BerlinVerified email at tu-berlin.de
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT) and University of Oslo (UiO)Verified email at singaporetech.edu.sg
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Shoaib Ahmed SiddiquiUniversity of CambridgeVerified email at cam.ac.uk
Clayton ScottProfessor of Electrical Engineering and Computer Science, University of MichiganVerified email at umich.edu
Billy Joe FranksPhD student of Computer Science, TU KaiserslauternVerified email at cs.uni-kl.de
Stephan MandtAssociate Professor, University of California, IrvineVerified email at uci.edu
Jacob KauffmannTU BerlinVerified email at tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Thomas DietterichDistinguished Professor (Emeritus), Computer Science, Oregon State UniversityVerified email at cs.orst.edu
Grégoire MontavonFreie Universität BerlinVerified email at fu-berlin.de
Philipp LiznerskiPhD Student, TU KaiserslauternVerified email at cs.uni-kl.de
Rodrigo Augusto da Silva AlvesAssistant Professor (Odborný asistent)Verified email at fit.cvut.cz
Robert BamlerUniveristy of Tübingen, GermanyVerified email at uni-tuebingen.de
Michael BortzFraunhofer ITWMVerified email at itwm.fraunhofer.de
Hakan BilenSchool of Informatics, University of EdinburghVerified email at ed.ac.uk

Robert A. Vandermeulen

Technische Universität Berlin & BIFOLD

Verified email at tu-berlin.de - Homepage

Machine Learning Nonparametric Statistics


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Deep One-Class Classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International Conference on Machine Learning, 4390-4399, 2018	1469	2018
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 109 (5), 756-795, 2021	486	2021
Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... International Conference on Learning Representations, 2019	401	2019
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	210	2018
Explainable deep one-class classification P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller International Conference on Learning Representations, 2020	134	2020
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	66	2021
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	58	2020
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	46	2019
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	19	2019
Consistency of robust kernel density estimators R Vandermeulen, C Scott Conference on Learning Theory, 568-591, 2013	19	2013
Transfer-based semantic anomaly detection L Deecke, L Ruff, RA Vandermeulen, H Bilen International Conference on Machine Learning, 2546-2558, 2021	18	2021
An Operator Theoretic Approach to Nonparametric Mixture Models RA Vandermeulen, CD Scott Annals of Statistics 47 (5), 2704-2733, 2019	18	2019
Anomaly detection with generative adversarial networks, 2018 L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft URL https://openreview. net/forum, 2018	14	2018
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations A Ritchie, RA Vandermeulen, C Scott Advances in Neural Information Processing Systems 33, 2020	13	2020
On the identifiability of mixture models from grouped samples RA Vandermeulen, CD Scott arXiv preprint arXiv:1502.06644, 2015	9	2015
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft Transactions on Machine Learning Research, 2022	7	2022
Robust kernel density estimation by scaling and projection in hilbert space RA Vandermeulen, C Scott Advances in Neural Information Processing Systems 27, 2014	7	2014
Deep Anomaly Detection by Residual Adaptation L Deecke, L Ruff, RA Vandermeulen, H Bilen	5*
VICE: Variational Inference for Concept Embeddings L Muttenthaler, CY Zheng, P McClure, RA Vandermeulen, MN Hebart, ... Advances in NeurIPS, 2022	4*	2022
A Proposal for Supervised Density Estimation RA Vandermeulen, R Saitenmacher, A Ritchie NeurIPS Pre-Registration Workshop, 2020	4	2020

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