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A Learning Framework for Distribution-Based Game-Theoretic ..:
, In:
Proceedings of the 21st ACM Conference on Economics and Computation
,
Jha, Tushant
;
Zick, Yair
- p. 355-377 , 2020
Link:
https://dl.acm.org/doi/10.1145/3391403.3399509
RT T1 <cTitle>Proceedings of the 21st ACM Conference on Economics and Computation</cTitle>: T1 <Title>A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3399509&Exemplar=1&LAN=DE A1 Jha, Tushant A1 Zick, Yair PB ACM YR 2020 K1 economic learning K1 sample complexity K1 solution concepts K1 Theory of computation K1 Theory and algorithms for application domains K1 Machine learning theory K1 Algorithmic game theory and mechanism design SP 355 OP 377 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3391403.3399509 DO https://dl.acm.org/doi/10.1145/3391403.3399509 SF ELIB - SuUB Bremen
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