BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IFDS - ECPv6.0.1.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IFDS
X-ORIGINAL-URL:https://ifds.info
X-WR-CALDESC:Events for IFDS
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20220313T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20221106T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220114T123000
DTEND;TZID=America/Los_Angeles:20220114T133000
DTSTAMP:20260425T155744
CREATED:20220325T200020Z
LAST-MODIFIED:20220325T200040Z
UID:1923-1642163400-1642167000@ifds.info
SUMMARY:ML Opt@ UW: Yue Sun
DESCRIPTION:Speaker: Yue Sun   \nTitle: Analysis of Policy Gradient Descent for Control: Global Optimality via Convex Parameterization   \nAbstract: Policy gradient descent is a popular approach in reinforcement learning due to its simplicity. Recent work has investigated the optimality and convergence properties of this method when applied in certain control problems. In this work\, we connect policy gradient descent (applied to a nonconvex problem formulation) with classical convex parameterizations in control theory\, to show the gradient dominance property for the nonconvex cost function. Such a connection between nonconvex and convex landscapes holds for continuous/discrete time LQR\, distributed optimal control\, minimizing the $cL_2$ gain\, among others. To the best of our knowledge\, this work offers the first result unifying the landscape analysis of a broad class of control problems.
URL:https://ifds.info/event/ml-opt-uw-yue-sun/
CATEGORIES:MLOpt@UWash
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220121T123000
DTEND;TZID=America/Chicago:20220121T133000
DTSTAMP:20260425T155744
CREATED:20220325T195836Z
LAST-MODIFIED:20220325T195856Z
UID:1919-1642768200-1642771800@ifds.info
SUMMARY:ML Opt@ UW: Lang Liu
DESCRIPTION:Speaker: Lang Liu \nTitle: The Sample Complexity of Statistical Comparison Between Generative Models \nAbstract: The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models. Although practically successful\, the sample complexity of the empirical estimator of divergence frontiers is unknown. We establish non-asymptotic bounds on the sample complexity of divergence frontiers\, providing theoretical guidance on their estimation procedure.
URL:https://ifds.info/event/ml-opt-uw-lang-liu/
CATEGORIES:MLOpt@UWash
END:VEVENT
END:VCALENDAR