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Volumn , Issue , 2011, Pages

NEWTRON: An efficient bandit algorithm for online multiclass prediction

Author keywords

[No Author keywords available]

Indexed keywords

BANDIT FEEDBACKS; MULTICLASS PREDICTION; NEWTON'S METHODS; NOVEL APPLICATIONS; PARAMETERIZED;

EID: 85162453290     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (50)

References (14)
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    • Abernethy, J.1    Hazan, E.2    Rakhlin, A.3
  • 3
    • 35448960376 scopus 로고    scopus 로고
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    • Baruch Awerbuch and Robert Kleinberg. Online linear optimization and adaptive routing. J. Comput. Syst. Sci., 74(1):97-114, 2008.
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    • Awerbuch, B.1    Kleinberg, R.2
  • 4
    • 84898768231 scopus 로고    scopus 로고
    • An efficient bandit algorithm for p T-regret in online multiclass prediction?
    • Jacob Abernethy and Alexander Rakhlin. An efficient bandit algorithm for p T-regret in online multiclass prediction? In COLT, 2009.
    • (2009) COLT
    • Abernethy, J.1    Rakhlin, A.2
  • 5
    • 80053461043 scopus 로고    scopus 로고
    • Multiclass classification with bandit feedback using adaptive regularization
    • Koby Crammer and Claudio Gentile. Multiclass classification with bandit feedback using adaptive regularization. In ICML, 2011.
    • (2011) ICML
    • Crammer, K.1    Gentile, C.2
  • 6
    • 33244456637 scopus 로고    scopus 로고
    • Robbing the bandit: Less regret in online geometric optimization against an adaptive adversary
    • Varsha Dani and Thomas P. Hayes. Robbing the bandit: less regret in online geometric optimization against an adaptive adversary. In SODA, pages 937-943, 2006.
    • (2006) SODA , pp. 937-943
    • Dani, V.1    Hayes, T.P.2
  • 7
    • 70349295143 scopus 로고    scopus 로고
    • The price of bandit information for online optimization
    • Varsha Dani, Thomas Hayes, and Sham Kakade. The price of bandit information for online optimization. In NIPS. 2007.
    • (2007) NIPS
    • Dani, V.1    Hayes, T.2    Kakade, S.3
  • 8
    • 20744454447 scopus 로고    scopus 로고
    • Online convex optimization in the bandit setting: Gradient descent without a gradient
    • Abraham D. Flaxman, Adam Tauman Kalai, and H. Brendan McMahan. Online convex optimization in the bandit setting: gradient descent without a gradient. In SODA, pages 385-394, 2005.
    • (2005) SODA , pp. 385-394
    • Flaxman, A.D.1    Kalai, A.T.2    McMahan, H.B.3
  • 9
    • 35348918820 scopus 로고    scopus 로고
    • Logarithmic regret algorithms for online convex optimization
    • Elad Hazan, Amit Agarwal, and Satyen Kale. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3):169-192, 2007.
    • (2007) Machine Learning , vol.69 , Issue.2-3 , pp. 169-192
    • Hazan, E.1    Agarwal, A.2    Kale, S.3
  • 11
    • 56449104477 scopus 로고    scopus 로고
    • Efficient bandit algorithms for online multiclass prediction
    • Sham M. Kakade, Shai Shalev-Shwartz, and Ambuj Tewari. Efficient bandit algorithms for online multiclass prediction. In ICML'08, pages 440-447, 2008.
    • (2008) ICML'08 , pp. 440-447
    • Kakade, S.M.1    Shalev-Shwartz, S.2    Tewari, A.3
  • 12
    • 77956144722 scopus 로고    scopus 로고
    • The epoch-greedy algorithm for multi-armed bandits with side information
    • John Langford and Tong Zhang. The epoch-greedy algorithm for multi-armed bandits with side information. In NIPS, 2007.
    • (2007) NIPS
    • Langford, J.1    Zhang, T.2
  • 13
    • 9444257628 scopus 로고    scopus 로고
    • Online geometric optimization in the bandit setting against an adaptive adversary
    • H. Brendan McMahan and Avrim Blum. Online geometric optimization in the bandit setting against an adaptive adversary. In COLT, pages 109-123, 2004.
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  • 14
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    • Closing the gap between bandit and full-information online optimization: High-probability regret bound
    • EECS Department, University of California, Berkeley, Aug
    • Alexander Rakhlin, Ambuj Tewari, and Peter Bartlett. Closing the gap between bandit and full-information online optimization: High-probability regret bound. Technical Report UCB/EECS-2007-109, EECS Department, University of California, Berkeley, Aug 2007.
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    • Rakhlin, A.1    Tewari, A.2    Bartlett, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.