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DTSTAMP:20260624T122121Z
DESCRIPTION:Title: Logarithmic divergence: from finance to optimal transpor
 t and information geometry\n\n\n	Abstract:\n\n\nDivergences\, such as the K
 ullback-Leibler divergence\, are distance-like quantities which arise in m
 any applications in probability\, statistics and data science. We introduc
 e a family of logarithmic divergences which is a non-linear extension of t
 he celebrated Bregman divergence. It is defined for any exponentially conc
 ave function (a function whose exponential is concave). We motivate this d
 ivergence by mathematical finance and large deviations of Dirichlet proces
 s. It also arises naturally from the solution to an optimal transport prob
 lem. The logarithmic divergence enjoys remarkable mathematical properties 
 including a generalized Pythagorean theorem in the sense of information ge
 ometry\, and induces a generalized exponential family of probability densi
 ties. In the last part of the talk we present a new differential geometric
  framework which connects optimal transport and information geometry. Join
 t works with Soumik Pal and Jiaowen Yang.\n\n\n	Speaker\n\n\nTing-Kam Leona
 rd Wong is an assistant professor at the Department of Computer and Mathem
 atical Sciences\, University of Toronto Scarborough and the Department of 
 Statistical Sciences\, University of Toronto. He received his PhD in mathe
 matics from University of Washington in 2016 under the supervison of Soumi
 k Pal. Before coming to Toronto in July 2018\, he was a non-tenure track a
 ssistant professor in financial mathematics at the Department of Mathemati
 cs\, University of Southern California.\n
DTSTART:20191115T203000Z
DTEND:20191115T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Ting-Kam Leonard Wong 
URL:/mathstat/channels/event/ting-kam-leonard-wong-302
 620
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