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BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.18.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Penn Engineering Events X-ORIGINAL-URL:https://seasevents.nmsdev7.com X-WR-CALDESC:Events for Penn Engineering Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20180311T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20181104T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20191025T150000 DTEND;TZID=America/New_York:20191025T160000 DTSTAMP:20191021T132453Z CREATED:20191021T132453Z LAST-MODIFIED:20191021T132453Z UID:2175-1572015600-1572019200@seasevents.nmsdev7.com SUMMARY:PRiML Seminar: "Nonconvex Optimization Meets Statistics: A Few Recent Stories" DESCRIPTION:Recent years have seen a flurry of activity in solving statistical estimation and learning problems via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima\, simple iterative methods such as gradient descent have been remarkably successful in practice. The theoretical footings\, however\, had been largely lacking until recently. This talk presents two recent stories about nonconvex statistical estimation\, which highlight the important role of statistical models in enabling efficient nonconvex optimization. The first story is about randomly initialized nonconvex methods for a phase retrieval problem: even without careful initialization\, simple algorithms like gradient descent provably find the global solution within a logarithmic number of iterations. The second story is concerned with uncertainty quantification for nonconvex low-rank matrix completion. We develop a de-biased estimator — on the basis of a nonconvex estimate — that enables optimal construction of confidence intervals for the missing entries of the unknown matrix. All of this is achieved via a leave-one-out statistical analysis framework\, which is very powerful in handling and decoupling complicated statistical dependency.\n\n\nThis is joint work with Cong Ma\, Yuling Yan\, Yuejie Chi\, and Jianqing Fan. URL:https://seasevents.nmsdev7.com/event/priml-seminar-nonconvex-optimization-meets-statistics-a-few-recent-stories/ LOCATION:Room 401B\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States CATEGORIES:Seminar ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu END:VEVENT END:VCALENDAR