基本信息
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Career Trajectory
Bio
Marius Kloft is interested in theory and algorithms of statistical machine learning and its applications, especially in statistical genetics, mechanical engineering, and chemical process engineering. He has been working on, e.g., multiple kernel learning, transfer learning, anomaly detection, extreme classification, and adversarial learning. He co-organized workshops on these topics at NIPS 2010, 2013, 2014, 2017, ICML 2016, and Dagstuhl 2018. His dissertation on Lp-norm multiple kernel learning was nominated by TU Berlin for the Doctoral Dissertation Award of the German Chapter of the ACM (GI). He received the Google Most Influential Papers Award and the DFG Emmy-Noether Career Award.
Research Interests
Papers共 164 篇Author StatisticsCo-AuthorSimilar Experts
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International Conference on Computational Linguisticspp.15802-15822, (2024)
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Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg,Marius Kloft, Oliver Buchstab,David Horst,Maximilian Alber,Lukas Ruff,Klaus-Robert Müller,
arxiv(2024)
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CoRR (2024)
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EXPERT SYSTEMS WITH APPLICATIONS (2024): 121164-121164
Rodrigo Alves,Antoine Patrick Isabelle Eric Ledent,Renato M. Assunção, Pedro Vaz-De-Melo,Marius Kloft
WWW 2024pp.2464-2475, (2024)
International Conference on Artificial Intelligence and Statisticspp.4528-4536, (2024)
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Saurabh Varshneya,Antoine Ledent,Philipp Liznerski, Andriy Balinskyy,Purvanshi Mehta,Waleed Mustafa,Marius Kloft
CoRR (2024)
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Fabian Hartung,Billy Joe Franks, Tobias Michels, Dennis Wagner,Philipp Liznerski, Steffen Reithermann,Sophie Fellenz,Fabian Jirasek,Maja Rudolph,Daniel Neider,Heike Leitte,Chen Song,
CoRR (2023)
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MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: APPLIED DATA SCIENCE AND DEMO TRACK, ECML PKDD 2023, PT VI (2023): 497-512
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