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Volumn , Issue , 2013, Pages 494-499

On optimal foraging and multi-armed bandits

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICS;

EID: 84897584848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Allerton.2013.6736565     Document Type: Conference Paper
Times cited : (33)

References (20)
  • 1
    • 84874045238 scopus 로고    scopus 로고
    • Regret analysis of stochastic and nonstochastic multi-armed bandit problems
    • S. Bubeck and N. Cesa-Bianchi. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Machine Learning, 5(1):1-122, 2012
    • (2012) Machine Learning , vol.5 , Issue.1 , pp. 1-122
    • Bubeck, S.1    Cesa-Bianchi, N.2
  • 2
    • 0002899547 scopus 로고
    • Asymptotically efficient adaptive allocation rules
    • T. L. Lai and H. Robbins. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6(1):4-22, 1985
    • (1985) Advances in Applied Mathematics , vol.6 , Issue.1 , pp. 4-22
    • Lai, T.L.1    Robbins, H.2
  • 3
    • 0036568025 scopus 로고    scopus 로고
    • Finite-time analysis of the multiarmed bandit problem
    • P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2):235-256, 2002
    • (2002) Machine Learning , vol.47 , Issue.2 , pp. 235-256
    • Auer, P.1    Cesa-Bianchi, N.2    Fischer, P.3
  • 4
    • 84860236413 scopus 로고    scopus 로고
    • Informationtheoretic regret bounds for Gaussian process optimization in the bandit setting
    • N. Srinivas, A. Krause, S. M. Kakade, and M. Seeger. Informationtheoretic regret bounds for Gaussian process optimization in the bandit setting. IEEE Transactions on Information Theory, 58(5):3250-3265, 2012
    • (2012) IEEE Transactions on Information Theory , vol.58 , Issue.5 , pp. 3250-3265
    • Srinivas, N.1    Krause, A.2    Kakade, S.M.3    Seeger, M.4
  • 5
  • 7
    • 0024089489 scopus 로고
    • Asymptotically efficient adaptive allocation rules for the multi-armed bandit problem with switching cost
    • R. Agrawal, M. V. Hedge, and D. Teneketzis. Asymptotically efficient adaptive allocation rules for the multi-armed bandit problem with switching cost. IEEE Transactions on Automatic Control, 33(10):899-906, 1988
    • (1988) IEEE Transactions on Automatic Control , vol.33 , Issue.10 , pp. 899-906
    • Agrawal, R.1    Hedge, M.V.2    Teneketzis, D.3
  • 8
    • 77955660815 scopus 로고    scopus 로고
    • Regret bounds for sleeping experts and bandits
    • R. Kleinberg, A. Niculescu-Mizil, and Y. Sharma. Regret bounds for sleeping experts and bandits. Machine learning, 80(2-3):245-272, 2010
    • (2010) Machine Learning , vol.80 , Issue.2-3 , pp. 245-272
    • Kleinberg, R.1    Niculescu-Mizil, A.2    Sharma, Y.3
  • 16
    • 34948834122 scopus 로고
    • Test of optimal sampling by foraging great tits
    • J. R. Krebs, A. Kacelnik, and P. Taylor. Test of optimal sampling by foraging great tits. Nature, 275(5675):27-31, 1978
    • (1978) Nature , vol.275 , Issue.5675 , pp. 27-31
    • Krebs, J.R.1    Kacelnik, A.2    Taylor, P.3
  • 17
    • 0036862934 scopus 로고    scopus 로고
    • Bees in two-armed bandit situations: Foraging choices and possible decision mechanisms
    • T. Keasar, E. Rashkovich, D. Cohen, and A. Shmida. Bees in two-armed bandit situations: Foraging choices and possible decision mechanisms. Behavioral Ecology, 13(6):757-765, 2002
    • (2002) Behavioral Ecology , vol.13 , Issue.6 , pp. 757-765
    • Keasar, T.1    Rashkovich, E.2    Cohen, D.3    Shmida, A.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.