BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Applied Mathematics and Statistics - ECPv6.2.8.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Applied Mathematics and Statistics
X-ORIGINAL-URL:https://engineering.jhu.edu/ams
X-WR-CALDESC:Events for Department of Applied Mathematics and Statistics
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:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T133000
DTEND;TZID=America/New_York:20220127T143000
DTSTAMP:20231210T182129
CREATED:20220120T153918Z
LAST-MODIFIED:20220125T173638Z
UID:39249-1643290200-1643293800@engineering.jhu.edu
SUMMARY:AMS Weekly Seminar w/ Alex Wein (Georgia Tech) on Zoom
DESCRIPTION:Title: Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials \nAbstract: A central goal in modern data science is to design algorithms for statistical inference tasks such as community detection\, high-dimensional clustering\, sparse PCA\, and many others. Ideally these algorithms would be both statistically optimal and computationally efficient. However\, it often seems impossible to achieve both these goals simultaneously: for many problems\, the optimal statistical procedure involves a brute force search while all known polynomial-time algorithms are statistically sub-optimal (requiring more data or higher signal strength than is information-theoretically necessary). In the quest for optimal algorithms\, it is therefore important to understand the fundamental statistical limitations of computationally efficient algorithms. \nI will discuss an emerging theoretical framework for understanding these questions\, based on studying the class of “low-degree polynomial algorithms.” This is a powerful class of algorithms that captures the best known poly-time algorithms for a wide variety of statistical tasks. This perspective has led to the discovery of many new and improved algorithms\, and also many matching lower bounds: we now have tools to prove failure of all low-degree algorithms\, which provides concrete evidence for inherent computational hardness of statistical problems. This line of work illustrates that low-degree polynomials provide a unifying framework for understanding the computational complexity of a wide variety of statistical tasks\, encompassing hypothesis testing\, estimation\, and optimization. \nHere is the zoom link is: https://wse.zoom.us/j/95448608570
URL:https://engineering.jhu.edu/ams/event/ams-seminar-w-alex-wein-nyu-or-maryland-110-or-on-zoom/
END:VEVENT
END:VCALENDAR