The contraction inequality for Rademacher averages is extended to Lipschitz functions with vector-valued domains, and it is also shown that in the bounding expression the Rademacher variables can be replaced by arbitrary iid symmetric and sub-gaussian variables. Example applications are given for multi-category learning, K-means clustering and learning-to-learn.
CITATION STYLE
Maurer, A. (2016). A vector-contraction inequality for rademacher complexities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9925 LNAI, pp. 3–17). Springer Verlag. https://doi.org/10.1007/978-3-319-46379-7_1
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