BEGIN:VCALENDAR VERSION:2.0 PRODID:-//https://caida.ubc.ca//NONSGML iCalcreator 2.41.92// CALSCALE:GREGORIAN METHOD:PUBLISH UID:62353231-6436-4535-a264-616133663161 X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde X-WR-TIMEZONE:America/Vancouver X-WR-CALNAME:The Non-Stochastic Control Framework - Naman Agarwal\, Resear ch Scientist\, Google AI\, Princeton BEGIN:VTIMEZONE TZID:America/Vancouver TZUNTIL:20221106T090000Z BEGIN:STANDARD TZNAME:PST DTSTART:20191103T020000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RDATE:20201101T020000 RDATE:20211107T020000 END:STANDARD BEGIN:DAYLIGHT TZNAME:PDT DTSTART:20200308T020000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RDATE:20210314T020000 RDATE:20220313T020000 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:afae0404-15f8-4efb-843a-45f8be538260 DTSTAMP:20260227T085051Z CLASS:PUBLIC CREATED:20200918T214821Z DESCRIPTION:Please register for this event here Abstract: Linear dynamical systems are a continuous subclass of reinforcement learning models that ar e widely used in robotics\, finance\, engineering\, and meteorology. Class ical control\, has focused on dynamics with Gaussian i.i.d. Noise and quad ratic loss functions in terms of provably efficient algorithms. I will pre sent a non-stochastic control framework inspired by online learning\, that generalizes some traditional notions of robust control and discuss method ology which achieves efficient control with adversarial noise and general convex loss functions… DTSTART;TZID=America/Vancouver:20201005T140000 DTEND;TZID=America/Vancouver:20201005T150000 LAST-MODIFIED:20210610T230231Z LOCATION:Please register to receive the Zoom link SUMMARY:The Non-Stochastic Control Framework - Naman Agarwal\, Research Sci entist\, Google AI\, Princeton TRANSP:OPAQUE URL:https://caida.ubc.ca/event/non-stochastic-control-framework-naman-agarw al-research-scientist-google-ai-princeton END:VEVENT END:VCALENDAR