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Probability inequalities for the sum of independent random variables
G. Bennett · 1962
Earlier work this paper cites.
Probability inequalities for sums of bounded random variables
W. Hoeffding · 1963
Earlier work this paper cites.
The tight constant in the dvoretzky-kiefer-wolfowitz inequality
P. Massart · 1990
Earlier work this paper cites.
Empirical berstein stopping
V. Mnih, C. Szepesv’ari, and J. Audibert · 2008
Earlier work this paper cites.
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
J. Audibert, R. Munos, and C. Szepesv’ari · 2009
Earlier work this paper cites.
Empirical bernstein bounds and sample variance penalization
A. Maurer and M. Pontil · 2009
Earlier work this paper cites.
Distributionally robust optimization under moment uncertainty with application to data-driven problems
E. Delage and Y. Ye · 2010
Earlier work this paper cites.
Empirical Bernstein boosting
P. Shivaswamy and T. Jebara · 2010
Earlier work this paper cites.
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R. Berk · 2012
Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
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