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Learning Game-theoretic Models from Aggregate Behavioral Da..:

, In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems,

Chan, Hau ; Ortiz, Luis E. - p. 1891-1893 , 2018

Link: https://dl.acm.org/doi/10.5555/3237383.3238014

RT 
T1 <cTitle>Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems</cTitle>: T1 <Title>Learning Game-theoretic Models from Aggregate Behavioral Data with Applications to Vaccination Rates in Public Health</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3238014&Exemplar=1&LAN=DE
				
A1 Chan, Hau
A1 Ortiz, Luis E.
PB International Foundation for Autonomous Agents and Multiagent Systems
YR 2018
K1 cdc
K1 game theory
K1 game-theoretic models
K1 generative model
K1 kl
K1 learning
K1 mixed strategies
K1 mle
K1 public health
K1 vaccination games
K1 vaccination rates
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Unsupervised learning
K1 Algorithmic game theory and mechanism design
K1 Algorithmic game theory
K1 Unsupervised learning and clustering
SP 1891
OP 1893
LK http://dx.doi.org/https://dl.acm.org/doi/10.5555/3237383.3238014
DO https://dl.acm.org/doi/10.5555/3237383.3238014
SF ELIB - SuUB Bremen

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