An extension of McDiarmid's inequality
November 17, 2015 ยท Declared Dead ยท ๐ International Symposium on Information Theory
"No code URL or promise found in abstract"
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Authors
Richard Combes
arXiv ID
1511.05240
Category
cs.LG: Machine Learning
Cross-listed
math.PR,
math.ST
Citations
37
Venue
International Symposium on Information Theory
Last Checked
6 months ago
Abstract
We generalize McDiarmid's inequality for functions with bounded differences on a high probability set, using an extension argument. Those functions concentrate around their conditional expectations. We further extend the results to concentration in general metric spaces.
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