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In the problem of aggregation, the aim is to combine a given class of base predictors to achieve predictions nearly as accurate as the best one.
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Optimal learning with Bernstein online aggregation
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Optimal bounds for aggregation of affine estimators
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Optimal rates for regularization of statistical inverse learning problems
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Localization of VC classes: Beyond local Rademacher complexities
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An unrestricted learning procedure
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High-dimensional statistics: A non-asymptotic viewpoint
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Exponential tail local Rademacher complexity risk bounds without the Bernstein condition
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An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
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A regret-variance trade-off in online learning
D. Van der Hoeven, N. Zhivotovskiy, and N. Cesa-Bianchi · 2022
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Online-to-PAC conversions: Generalization bounds via regret analysis
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