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We analyze general model selection procedures using penalized empirical loss minimization under computational constraints.
A stochastic approximation method
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Uniform Central Limit Theorems
R. M. Dudley · 1999
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Smooth discrimination analysis
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Finite-time analysis of the multiarmed bandit problem
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Rademacher and Gaussian complexities: Risk bounds and structural results
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Model selection and error estimation
P. L. Bartlett, S. Boucheron, and G. Lugosi · 2002
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The nonstochastic multiarmed bandit problem
Complexity regularization via localized random penalties
G. Lugosi and M. Wegkamp · 2004
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Local rademacher complexities
P. Bartlett, O. Bousquet, and S. Mendelson · 2005
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Convexity, classification, and risk bounds
P. Bartlett, M. Jordan, and J. McAuliffe · 2006
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Empirical minimization
P. L. Bartlett and S. Mendelson · 2006
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Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
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Fast rates for estimation error and oracle inequalities for model selection
P. L. Bartlett · 2008
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Robust stochastic approximation approach to stochastic programming
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P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire · 2003
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Concentration inequalities and model selection
P. Massart · 2003
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On the generalization ability of on-line learning algorithms
N. Cesa-Bianchi, A. Conconi, and C. Gentile · 2004
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Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization
V. Koltchinskii
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Local Rademacher complexities and oracle inequalities in risk minimization
V. Koltchinskii
Cited in the paper.
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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Statistics for High-Dimensional Data
P. Bühlmann and S. van de Geer · 2011
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