Marius Kloft

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Citations	10532	8537
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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Alexander BinderChair Computer Vision, OvGU, Magdeburg, GermanyVerified email at ovgu.de
Ulf BrefeldLeuphana Universität LüneburgVerified email at leuphana.de
Soeren SonnenburgMachine Learning ResearcherVerified email at tomtom.com
Pavel LaskovUniversity of LiechtensteinVerified email at uni.li
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Konrad RieckTechnische Universität BerlinVerified email at tu-berlin.de
Gilles BlanchardProfessor of Statistics, University of Paris-SaclayVerified email at universite-paris-saclay.fr
Christian WidmerGoogleVerified email at google.com
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.Verified email at google.com
Corinna CortesGoogle Research, NYVerified email at google.com
Robert JenssenVisual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPHVerified email at uit.no
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Xinghua Lou, Ph.D.Vicarious AIVerified email at vicarious.com
Kristof T. SchüttPfizer, Machine Learning ResearchVerified email at pfizer.com

Marius Kloft

Professor, RPTU Kaiserslautern-Landau

Verified email at cs.uni-kl.de - Homepage

Machine Learning Anomaly Detection Deep Learning Learning Theory


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" A unifying view of multiple kernel learning", Technical report M KLOFT arXiv 1005, 437, 2010		2010
" DeepIntegrate" Integration heterogener Datenquellen im Deep Learning: Architekturen, Algorithmen und Anwendung in der Pflanzenzüchtung: Veröffentlichung der Ergebnisse von … M Kloft, M Enders, S Varshneya Technische Universität Kaiserslautern, 2022		2022
12 Structured Learning from Cheap Data X Lou, M Kloft, G Rätsch, FA Hamprecht Advanced Structured Prediction, 281, 2014		2014
3.6 On the Need of Theory and Algorithms Correcting for Confouding Factors M Kloft Machine Learning with Interdependent and Non-identically Distributed Data, 36, 0
35TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING, ICML 2018 T Rashid, CS De Witt, G Farquhar, J Foerster, S Whiteson, M Samvelyan, ...		2018
4.3 Deep eXtreme Classification M Kloft Extreme Classification, 79, 0
A call for standardization and validation of text style transfer evaluation P Ostheimer, M Nagda, M Kloft, S Fellenz arXiv preprint arXiv:2306.00539, 2023	4	2023
A critical assessment of the importance of seedling age in the system of rice intensification (SRI) in eastern India D Deb, J Lässig, M Kloft Experimental agriculture 48 (3), 326-346, 2012	31	2012
A framework for quantitative security analysis of machine learning P Laskov, M Kloft Proceedings of the 2nd ACM workshop on Security and artificial intelligence, 1-4, 2009	73	2009
A Multi-Class Support Vector Machine based on Scatter Criteria R Jenssen, M Kloft, A Zien, S Sonnenburg, KR Müller	3	2009
A new scatter-based multi-class support vector machine R Jenssen, M Kloft, S Sonnenburg, A Zien, KR Müller 2011 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2011	1	2011
A poisoning attack against online anomaly detection M Kloft, P Laskov NIPS Workshop on Machine Learning in Adversarial Environments for Computer …, 2007	29	2007
A process planning system using deep artificial neural networks for the prediction of operation sequences M Hussong, S Varshneya, P Rüdiger-Flore, M Glatt, M Kloft, JC Aurich Procedia CIRP 120, 135-140, 2023		2023
A scatter-based prototype framework and multi-class extension of support vector machines R Jenssen, M Kloft, A Zien, S Sonnenburg, KR Müller PloS one 7 (10), e42947, 2012	13	2012
A systematic approach to random data augmentation on graph neural networks BJ Franks, M Anders, M Kloft, P Schweitzer arXiv preprint arXiv:2112.04314, 2021		2021
A systematic approach to universal random features in graph neural networks BJ Franks, M Anders, M Kloft, P Schweitzer Transactions on Machine Learning Research, 2023	1	2023
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	874	2021
A unifying view of multiple kernel learning M Kloft, U Rückert, PL Bartlett Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010	62	2010
Active and semi-supervised data domain description N Görnitz, M Kloft, U Brefeld Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009	44	2009
Active learning for network intrusion detection N Görnitz, M Kloft, K Rieck, U Brefeld Proceedings of the 2nd ACM workshop on Security and artificial intelligence …, 2009	96	2009

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