BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Mengdi Wang (Princeton University)
DTSTART:20200526T190000Z
DTEND:20200526T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/1
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/1/
 ">Statistical complexity of reinforcement learning</a>\nby Mengdi Wang (Pr
 inceton University) as part of Online Seminar of Mathematical Foundations 
 of Data Science\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yinyu Ye (Stanford University)
DTSTART:20200602T190000Z
DTEND:20200602T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/2/
 ">Distributionally robust optimization\, online linear programming and mar
 kets for public-good allocations</a>\nby Yinyu Ye (Stanford University) as
  part of Online Seminar of Mathematical Foundations of Data Science\n\nAbs
 tract: TBA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robert M. Freund (MIT)
DTSTART:20200609T190000Z
DTEND:20200609T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/3/
 ">From stochastic Frank-Wolfe to the ellipsoid method: Recent progress on 
 practical optimization in data science (the Frank-Wolfe Method) and theore
 tical optimization (the ellipsoid method)</a>\nby Robert M. Freund (MIT) a
 s part of Online Seminar of Mathematical Foundations of Data Science\n\nAb
 stract: TBA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sham Kakade (University of Washington\, Seattle)
DTSTART:20200616T190000Z
DTEND:20200616T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/4
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/4/
 ">Representation\, Modeling\, and Gradient Based Optimization in Reinforce
 ment Learning</a>\nby Sham Kakade (University of Washington\, Seattle) as 
 part of Online Seminar of Mathematical Foundations of Data Science\n\n\nAb
 stract\nReinforcement learning is now the dominant paradigm for how an age
 nt learns to interact with the world. The approach has lead to successes r
 anging across numerous domains\, including game playing and robotics\, and
  it holds much promise in new domains\, from self driving cars to interact
 ive medical applications. Some of the central challenges are:\n\n- Represe
 ntational learning: does having a good representation of the environment p
 ermit efficient reinforcement learning?\n\n- Modeling: should we explicitl
 y build a model of our environment or\, alternatively\, should we directly
  learn how to act?\n\n- Optimization: in practice\, deployed algorithms of
 ten use local search heuristics. Can we provably understand  when these ap
 proaches are effective and provide faster and more robust alternatives?\n\
 nThis talk will survey a number of results on these basic questions. Throu
 ghout\, we will  highlight the interplay of theory\, algorithm design\, an
 d practice.\n
LOCATION:https://researchseminars.org/talk/OptMLStat/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robert Nowak (University of Wisconsin\, Madison)
DTSTART:20200623T190000Z
DTEND:20200623T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/5
DESCRIPTION:by Robert Nowak (University of Wisconsin\, Madison) as part of
  Online Seminar of Mathematical Foundations of Data Science\n\nAbstract: T
 BA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alex Shapiro (Georgia Institute of Technology)
DTSTART:20200702T190000Z
DTEND:20200702T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/6
DESCRIPTION:by Alex Shapiro (Georgia Institute of Technology) as part of O
 nline Seminar of Mathematical Foundations of Data Science\n\nAbstract: TBA
 \n
LOCATION:https://researchseminars.org/talk/OptMLStat/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adrian S. Lewis (Cornell University)
DTSTART:20200707T190000Z
DTEND:20200707T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/7/
 ">Smoothness in nonsmooth optimization</a>\nby Adrian S. Lewis (Cornell Un
 iversity) as part of Online Seminar of Mathematical Foundations of Data Sc
 ience\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael I. Jordan (UC Berkeley)
DTSTART:20200714T190000Z
DTEND:20200714T200000Z
DTSTAMP:20260422T225636Z
UID:OptMLStat/8
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/OptMLStat/8/
 ">Optimization with momentum: dynamical\, variational\, and symplectic per
 spectives</a>\nby Michael I. Jordan (UC Berkeley) as part of Online Semina
 r of Mathematical Foundations of Data Science\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/OptMLStat/8/
END:VEVENT
END:VCALENDAR