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DTSTART:20170326T010000
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DTSTART:20171029T010000
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DTSTART;TZID=Europe/Paris:20171009T140000
DTEND;TZID=Europe/Paris:20171009T151500
DTSTAMP:20260715T083929
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UID:11936-1507557600-1507562100@crest.science
SUMMARY:Zoltan SZABO (Ecole Polytechnique) - "Characteristic Tensor Kernels"
DESCRIPTION:\nThe Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 9th of October 2017\nPlace: Room 3001.\nZoltan SZABO (Ecole Polytechnique) – “Characteristic Tensor Kernels” \nAbstract: \nMaximum mean discrepancy (MMD) and Hilbert-Schmidt independence criterion (HSIC) are popular techniques in data science to measure the difference and the independence of random variables\, respectively. \nThanks to their kernel-based foundations\, MMD and HSIC are applicable on a variety of domains including documents\, images\, trees\, graphs\, time series\, mixture models\, dynamical systems\, sets\, distributions\, permutations. Despite their tremendous practical success\, quite little is known about when HSIC characterizes independence and MMD with tensor kernel can discriminate probability distributions\, in terms of the  contributing kernel components. In this talk\, I am going to present a complete answer to this question\, with conditions which are often easy to verify in practice. [Joint work with Bharath K. Sriperumbudur (PSU). \nPreprint: https://arxiv.org/abs/1708.08157]\nOrganizers:\nCristina BUTUCEA\, Alexandre TSYBAKOV\, Eric MOULINES\, Mathieu ROSENBAUM\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/nicolas-marie-modalx-paris-10esme-sudria-estimation-non-parametrique-dans-les-equations-differentielles-dirigees-par-le-mouvement-brownien-fractionnaire-2/
CATEGORIES:Statistics
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