BEGIN:VCALENDAR
PRODID:-//AddEvent Inc//AddEvent.com v1.7//EN
VERSION:2.0
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:STANDARD
DTSTART:20261101T010000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20260308T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION:Some Learning Bounds and Guarantees for Testing (Quantum) Hypotheses\n​\nMachine learning is a powerful tool\, yet we often do not how well a learning algorithm might perform on any given task. One standard approach to bound the accuracy of a learning algorithm is to reduce the learning task to hypothesis testing. Fano's inequality then states that a large amount of mutual information between the learner's observations and the set of unknown parameters is a necessary condition for success. In this talk I will describe how such a condition is also sufficient for succeeding at some learning task\, thereby providing a purely information-theoretic guarantee for learning. Noting that this guarantee has an immediate extension to quantum information theory\, I will then introduce the task of "testing quantum hypotheses" in which the unknown parameters of the learning task are prepared in a quantum register in superposition (rather than being sampled stochastically) and the learner's success at this task is measured by their ability to establish quantum correlations with that register. I will discuss ongoing attempts to characterize this scenario.
X-ALT-DESC;FMTTYPE=text/html:<strong>Some Learning Bounds and Guarantees for Testing (Quantum) Hypotheses</strong><br />​<br />Machine learning is a powerful tool, yet we often do not how well a learning algorithm might perform on any given task. One standard approach to bound the accuracy of a learning algorithm is to reduce the learning task to hypothesis testing. Fano's inequality then states that a large amount of mutual information between the learner's observations and the set of unknown parameters is a necessary condition for success. In this talk I will describe how such a condition is also sufficient for succeeding at some learning task, thereby providing a purely information-theoretic guarantee for learning. Noting that this guarantee has an immediate extension to quantum information theory, I will then introduce the task of "testing quantum hypotheses" in which the unknown parameters of the learning task are prepared in a quantum register in superposition (rather than being sampled stochastically) and the learner's success at this task is measured by their ability to establish quantum correlations with that register. I will discuss ongoing attempts to characterize this scenario.
UID:1776802471addeventcom
SUMMARY:IQC Student Seminar Featuring Evan Peters
DTSTART;TZID=America/New_York:20230726T120000
DTEND;TZID=America/New_York:20230726T130000
DTSTAMP:20260421T201431Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:QNC 1201
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT30M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
END:VCALENDAR