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Learning bounded subsets of Lp

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Date

2021

Authors

Mendelson, Shahar

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

We study learning problems in which the underlying class is a bounded subset of Lp and the target Y belongs to Lp. Previously, minimax sample complexity estimates were known under such boundedness assumptions only when p = ∞. We present a sharp sample complexity estimate that holds for any p > 4; it is based on a learning procedure that is suited for heavy-tailed problems.

Description

Keywords

Statistical learning

Citation

URI

http://hdl.handle.net/1885/315977

Collections

ANU Research Publications

Source

IEEE Transactions on Information Theory

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1109/TIT.2021.3083553

Restricted until

2099-12-31

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Learning_Bounded_Subsets_of_L.pdf (312.59 KB)

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