Asymptotic theory of finite dimensional normed spaces
Asymptotic theory of finite dimensional normed spaces
IEEE Transactions on Information Theory
Rademacher averages and phase transitions in Glivenko-Cantelli classes
IEEE Transactions on Information Theory
Improving the sample complexity using global data
IEEE Transactions on Information Theory
A few notes on statistical learning theory
Advanced lectures on machine learning
Full length article: Approximation properties of certain operator-induced norms on Hilbert spaces
Journal of Approximation Theory
Minimax-optimal rates for sparse additive models over kernel classes via convex programming
The Journal of Machine Learning Research
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We investigate the fat-shattering dimension and the localized Rademacher averages of kernel machines and their connection to the eigenvalues associated with the kernel.