BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211129T144705
DTSTART;TZID=America/Detroit:20211203T100000
DTEND;TZID=America/Detroit:20211203T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Lester Mackey\, Machine Learning Researcher\, Microsoft Research New England\, Adjunct Professor\, Department of Statistics\, Stanford University
DESCRIPTION:Abstract: This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte Carlo thinning:\n\n1) Given an initial n point summary (for example\, from independent sampling or a Markov chain)\, kernel thinning finds a subset of only square-root n points with comparable worst-case integration error across a reproducing kernel Hilbert space.\n\n2) If the initial summary suffers from biases due to off-target sampling\, tempering\, or burn-in\, Stein thinning simultaneously compresses the summary and improves the accuracy by correcting for these biases.\n\nThese tools are especially well-suited for tasks that incur substantial downstream computation costs per summary point like organ and tissue modeling in which each simulation consumes 1000s of CPU hours. \n\n\nLester Mackey is a statistical machine learning researcher at Microsoft Research New England and an adjunct professor at Stanford University. His current research interests include statistical machine learning\, scalable algorithms\, high-dimensional statistics\, approximate inference\, and probability. Lately\, he has been developing and analyzing scalable learning algorithms for healthcare\, climate forecasting\, approximate posterior inference\, high-energy physics\, recommender systems\, and the social good.\n\nhttps://web.stanford.edu/~lmackey/
UID:84426-21623928@events.umich.edu
URL:https://events.umich.edu/event/84426
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:Off Campus Location
CONTACT:
END:VEVENT
END:VCALENDAR