This event is part of Decision Making and Uncertainty View Details

Machine Learning and Mean-Field Games

Description

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Multi-agent reinforcement learning (MARL) with incorporation of techniques and ideas from the theory of mean field games is one of the most active areas in learning and control. The purpose of this workshop is to bring leading experts and junior researchers to  showcase the latest developments in this interdisciplinary field.

Organizer

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X G
Xin Guo University of California, Berkeley

Speakers

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B A
Beatrice Acciaio ETH Zürich
H C
Haoyang Cao The Alan Turing Institute
R C
Rene Carmona Princeton University
R C
Rama Cont University of Oxford
C C
Christa Cuchiero University of Vienna
C G
Camilo Garcia University College London
O G
Oliver Gueant Université Paris 1 Pantheon Sorbonne
R H
Ruimeng Hu University of California, Santa Barbara
S J
Sebastian Jaimungal University of Toronto
D L
Daniel Lacker Columbia University
M L
Martin Larsson Carnegie-Mellon University
M L
Mathieu Lauriere Google Brain
C L
Charles Lehalle Abu Dhabi Investment Authority
S P
Sarah Perrin Université de Lille
H P
Huyen Pham University of Paris 6 and CNRS
Z R
Zhengjie Ren University Paris Dauphine – PSL
M R
Mathieu Rosenbaum Ecole Polytechnique
J R
Johannes Ruf London School of Economics
L S
Lukas Szpruch University of Edinburgh
J T
Josef Teichmann ETH Zürich
R X
Renyuan Xu University of Southern California
T Z
Thaleia Zariphoupoulou The University of Texas at Austin
Y Z
Yufei Zhang London School of Economics and Political Science
X Z
Xunyu Zhou Columbia University

Registration

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