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• W3104619653 abstract "We consider the problem of aggregating the elements of a possibly infinite dictionary for building a decision procedure that aims at minimizing a given criterion. Along with the dictionary, an independent identically distributed training sample is available, on which the performance of a given procedure can be tested. In a fairly general set-up, we establish an oracle inequality for the Mirror Averaging aggregate with any prior distribution. By choosing an appropriate prior, we apply this oracle inequality in the context of prediction under sparsity assumption for the problems of regression with random design, density estimation and binary classification." @default.
• W3104619653 created "2020-11-23" @default.
• W3104619653 creator A5025419743 @default.
• W3104619653 creator A5038868699 @default.
• W3104619653 date "2012-08-01" @default.
• W3104619653 modified "2023-10-18" @default.
• W3104619653 title "Mirror averaging with sparsity priors" @default.
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• W3104619653 doi "https://doi.org/10.3150/11-bej361" @default.
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