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A vector-contraction inequality for Rademacher complexities

arXiv 1605.00251 ↗First posted 1 May 2016cs.LG stat.MLcited by 141

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Abstract (arXiv)

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.

Key facts. Cited by 141 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 20; 2022: 21; 2023: 29; 2024: 22; 2025: 22; 2026: 26.

141 papers.

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38 more
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