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DTSTART;TZID=America/Los_Angeles:20200205T161500
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DTSTAMP:20260615T162707
CREATED:20190830T174047Z
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SUMMARY:Kernel approaches in global statistical distances\, local measure detection\, and active learning
DESCRIPTION:In this talk\, we’ll discuss the problem of constructing meaningful distances between probability distributions given only finite samples from each distribution.  We approach this through the use of data-adaptive and localized kernels\, and in a variety of contexts.  First\, we construct locally adaptive kernels to define fast pairwise distances between distributions\, with applications to unsupervised clustering.  Then\, we construct localized kernels to determine a statistical framework for determining where two distributions differ\, with applications to measure detection for generative models.  Finally\, we’ll begin to address the question of measure detection without a priori known labels of which distribution a point came from.  This is addressed through active learning\, in which one can choose a small number of points at which to query a label.  This is ongoing work with Xiuyuan Cheng (Duke) and Hrushikesh Mhaskar (CGU)\, among others.
URL:https://colleges.claremont.edu/ccms/event/alex-cloninger/
LOCATION:Freeberg Forum\, LC 62\, Kravis Center\, CMC
CATEGORIES:Colloquium
ORGANIZER;CN="Blerta Shtylla":MAILTO:shtyllab@pomona.edu
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