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Bio
Professor Manfred Opper is interested in the development and the theoretical analysis of methods for probabilistic inference in machine learning using techniques of statistical mechanics and statistics. In recent years he has worked especially in the area of data assimilation, where he has developed approximations for inference in stochastic differential equations, Markov jump processes and dynamical processes on large networks. He also works on the relation between inference and optimal stochastic control and on the application of random matrix theory to machine learning algorithms. He has over 170 publications, mostly in applications of statistical mechanics and related probabilistic methods to problems of machine learning and other complex systems. He is a member of the editorial board of Journal of Statistical Mechanics.
Research Interests
Papers共 220 篇Author StatisticsCo-AuthorSimilar Experts
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Entropy (Basel, Switzerland)no. 2 (2023): 316-316
arXiv (Cornell University) (2023)
Gabriel Nobis, Maximilian Springenberg,Marco Aversa, Michael Detzel,Rembert Daems,Roderick Murray-Smith,Shinichi Nakajima,Sebastian Lapuschkin,Stefano Ermon,Tolga Birdal,Manfred Opper, Christoph Knochenhauer,
arxiv(2023)
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