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DTSTART:20070311T020000
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DTSTAMP:20241209T100954
DTSTART;TZID=America/Detroit:20250110T100000
DTEND;TZID=America/Detroit:20250110T180000
SUMMARY:Other:Poster Sale
DESCRIPTION:Get ready to transform your space with the Annual Poster Sale! From January 6th to 10th\, 2025\, visit the Michigan Union between 10:00 AM and 6:00 PM to shop an incredible selection of posters. Whether you’re into iconic movies\, chart-topping musicians\, binge-worthy TV shows\, or stunning art\, there’s something for everyone.\n\nWhether you’re looking to personalize your dorm or find the perfect gift\, this is the event you don’t want to miss. With unbeatable prices and endless options\, there’s something for every taste and style.\n\nAdmission: Free to attend. Posters available for purchase.
UID:129345-21862490@events.umich.edu
URL:https://events.umich.edu/event/129345
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:art,CCI,center for campus involvement,umich,visual arts
LOCATION:Michigan Union - Willis Ward Lounge, 1st floor
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250106T153209
DTSTART;TZID=America/Detroit:20250110T100000
DTEND;TZID=America/Detroit:20250110T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Kabir Verchand\, Gary C. Butler Family Postdoctoral Fellow\, Georgia Institute of Technology and Cambridge University
DESCRIPTION:Abstract: Modern data pipelines are growing both in size and complexity\, introducing tradeoffs across various aspects of the associated learning challenges.  A key source of complexity\, and the focus of this talk\, is missing data.  Estimators designed to handle missingness often rely on strong assumptions about the mechanism by which data is missing\, such as that the data is missing completely at random (MCAR).  By contrast\, real data is rarely MCAR.  In the absence of these strong assumptions\, can we still trust these estimators?\n\nIn this talk\, I will present a framework that bridges the gap between the MCAR and assumption-free settings. This framework reveals an inherent tradeoff between estimation accuracy and robustness to modeling assumptions.  Focusing on the fundamental task of mean estimation\, I will then present estimators which optimally navigate this tradeoff\, offering both improved robustness and performance.\n\nhttps://kabirverchand.github.io/
UID:129237-21862365@events.umich.edu
URL:https://events.umich.edu/event/129237
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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