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We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error.
Piecewise-polynomial approximations of functions of the classes W p α {W}^{\alpha}_{p}
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Concentration inequalities for sub-additive functions using the entropy method
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Optimal aggregation of classifiers in statistical learning
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Empirical Bernstein boosting
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Smoothness, low noise and fast rates
N. Srebro, K. Sridharan, and A. Tewari · 2010
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Statistics for High-Dimensional Data: Methods, Theory and Applications
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Covering numbers of Gaussian reproducing kernel Hilbert spaces
T. Kühn · 2011
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Variance penalizing AdaBoost
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Robust solutions of optimization problems affected by uncertain probabilities
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Local Rademacher complexities
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Theory of classification: a survey of some recent advances
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Convexity, classification, and risk bounds
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Concentration Inequalities: a Nonasymptotic Theory of Independence
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A complete proof of universal inequalities for the distribution function of the binomial law
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Learning without concentration
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Oracle-based robust optimization via online learning
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Robust empirical optimization is almost the same as mean-variance optimization
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Quantifying input uncertainty in stochastic optimization
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Statistics of robust optimization: A generalized empirical likelihood approach
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