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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20180101T000000
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DTSTART;TZID=UTC:20190226T140000
DTEND;TZID=UTC:20190226T150000
DTSTAMP:20260404T121425
CREATED:20190214T121614Z
LAST-MODIFIED:20190214T143922Z
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SUMMARY:Seminar: Generalized maximum entropy estimation
DESCRIPTION:Title: Generalized maximum entropy estimation\nSpeaker: Dr David Sutter\nAffiliation: Institute for Theoretical Physics\, ETH Zurich\nLocation: 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints\, possibly corrupted by noise. Based on duality of convex programming\, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. \nThis is joint work with T. Sutter\, P. Esfahani\, and J. Lygeros (arXiv:1708.07311). \nBiography. Dr David Sutter is a postdoctoral researcher at ETH Zurich\, working on mathematical aspects of quantum information theory. He obtained his PhD degree from the Institute for Theoretical Physics at ETH Zurich under the supervision of Prof. Renato Renner. Dr Sutter’s interests lie in the intersection of quantum mechanics\, information theory\, and mathematical physics. To understand the fundamental limits of information processing and communications\, he utilizes tools from matrix analysis\, optimization theory and probability theory.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-generalized-maximum-entropy-estimation/
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