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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Shanghai
BEGIN:STANDARD
DTSTART:19910915T010000
TZOFFSETFROM:+0900
TZOFFSETTO:+0800
TZNAME:CST
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BEGIN:VEVENT
DTSTAMP:20260429T023827Z
UID:31698A39-A823-4296-8B78-CBCE6143D898
DTSTART;TZID=Asia/Shanghai:20260428T110000
DTEND;TZID=Asia/Shanghai:20260428T115500
DESCRIPTION:Since its introduction by John von Neumann in the 1940s\, game 
 theory has provided a powerful mathematical language for modeling systems 
 of interacting agents with potentially conflicting objectives. Unlike clas
 sical optimization\, which relies on a single global objective function\, 
 game theory shifts the focus to equilibrium as a fundamental organizing pr
 inciple for complex systems. In this talk\, I will present a long-standing
  research program developed over the past two decades that explores a game
 -theoretic perspective on machine learning and pattern recognition. The ce
 ntral idea is to interpret data elements—such as points\, labels\, or fe
 atures—as interacting agents\, whose relationships are encoded through p
 ayoff functions\, and whose collective behavior is described in terms of e
 quilibrium configurations. This viewpoint leads to principled formulations
  of clustering\, semi-supervised learning\, graph matching\, and contextua
 l classification\, and reveals deep connections between evolutionary dynam
 ics\, quadratic optimization\, and graph-based learning. Beyond offering a
  unifying conceptual framework\, equilibrium-based modeling provides natur
 al support for soft assignments\, robustness to noise\, and distributed co
 mputational schemes with well-defined stability properties. Applications t
 o computer vision illustrate how equilibrium thinking can complement\, and
  in some cases reshape\, traditional optimization-based approaches in mode
 rn AI.\n\nSpeaker(s): Marcello Pelillo\n\nGuangzhou\, Guangdong\, China
LOCATION:Guangzhou\, Guangdong\, China
ORGANIZER:cschenwn@scut.edu.cn
SEQUENCE:11
SUMMARY:From Optima to Equilibria: Game-theoretic Modelsof Pattern Analysis
  and Recognition.
URL;VALUE=URI:https://events.vtools.ieee.org/m/557345
X-ALT-DESC:Description: <br /><p>Since its introduction by John von Neumann
  in the 1940s\, game theory has provided a powerful mathematical language 
 for modeling systems of interacting agents with potentially conflicting ob
 jectives. Unlike classical optimization\, which relies on a single global 
 objective function\, game theory shifts the focus to equilibrium as a fund
 amental organizing principle for complex systems. In this talk\, I will pr
 esent a long-standing research program developed over the past two decades
  that explores a game-theoretic perspective on machine learning and patter
 n recognition. The central idea is to interpret data elements&mdash\;such 
 as points\, labels\, or features&mdash\;as interacting agents\, whose rela
 tionships are encoded through payoff functions\, and whose collective beha
 vior is described in terms of equilibrium configurations. This viewpoint l
 eads to principled formulations of clustering\, semi-supervised learning\,
  graph matching\, and contextual classification\, and reveals deep connect
 ions between evolutionary dynamics\, quadratic optimization\, and graph-ba
 sed learning. Beyond offering a unifying conceptual framework\, equilibriu
 m-based modeling provides natural support for soft assignments\, robustnes
 s to noise\, and distributed computational schemes with well-defined stabi
 lity properties. Applications to computer vision illustrate how equilibriu
 m thinking can complement\, and in some cases reshape\, traditional optimi
 zation-based approaches in modern AI.</p>
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