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The Ethereal
A vector-contraction inequality for Rademacher complexities
May 01, 2016 ยท Declared Dead ยท ๐ International Conference on Algorithmic Learning Theory
"No code URL or promise found in abstract"
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Authors
Andreas Maurer
arXiv ID
1605.00251
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
288
Venue
International Conference on Algorithmic Learning Theory
Last Checked
3 months ago
Abstract
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.
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