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NASA : achieving lower regrets and faster rates via adap..:

, In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining,

Ouyang, Hua ; Gray, Alexander - p. 159-167 , 2012

Link: https://dl.acm.org/doi/10.1145/2339530.2339557

RT 
T1 <cTitle>Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining</cTitle>: T1 <Title>NASA				: achieving lower regrets and faster rates via adaptive stepsizes</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-2339557&Exemplar=1&LAN=DE
				
A1 Ouyang, Hua
A1 Gray, Alexander
PB ACM
YR 2012
K1 adaptive learning
K1 online convex optimization
K1 online learning
K1 stochastic optimization
K1 Theory of computation
K1 Design and analysis of algorithms
K1 Online algorithms
K1 Online learning algorithms
K1 Models of computation
K1 Interactive computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Online learning theory
K1 Mathematical optimization
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Machine learning approaches
K1 Markov decision processes
K1 Mathematics of computing
K1 Mathematical analysis
SP 159
OP 167
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/2339530.2339557
DO https://dl.acm.org/doi/10.1145/2339530.2339557
SF ELIB - SuUB Bremen

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