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We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression.
Piecewise-polynomial approximations of functions of the classes W p α {W}_{p}^{\alpha}
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Optimal learning rates for kernel conjugate gradient regression
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G. Raskutti, M. Wainwright, and B. Yu · 2011
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On early stopping in gradient descent learning
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The masked sample covariance estimator: an analysis using matrix concentration inequalities
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Random design analysis of ridge regression
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Bootstrapping big data
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Minimax-optimal rates for sparse additive models over kernel classes via convex programming
G. Raskutti, M. J. Wainwright, and B. Yu · 2012
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Communication-efficient algorithms for statistical optimization
Y. Zhang, J. C. Duchi, and M. J. Wainwright · 2012
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Sharp analysis of low-rank kernel matrix approximations
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