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Aggregation by Exponential Weighting and Sharp Oracle Inequalities

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Learning Theory (COLT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4539))

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Abstract

In the present paper, we study the problem of aggregation under the squared loss in the model of regression with deterministic design. We obtain sharp oracle inequalities for convex aggregates defined via exponential weights, under general assumptions on the distribution of errors and on the functions to aggregate. We show how these results can be applied to derive a sparsity oracle inequality.

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Nader H. Bshouty Claudio Gentile

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Dalalyan, A.S., Tsybakov, A.B. (2007). Aggregation by Exponential Weighting and Sharp Oracle Inequalities. In: Bshouty, N.H., Gentile, C. (eds) Learning Theory. COLT 2007. Lecture Notes in Computer Science(), vol 4539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72927-3_9

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  • DOI: https://doi.org/10.1007/978-3-540-72927-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72925-9

  • Online ISBN: 978-3-540-72927-3

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