Fetching the paper…
Reading the bibliography…
Learning rates for least-squares regression are typically expressed in terms of $L_2$-norms.
Vector Measures , American Mathematical Society, Providence, 1977
J. Diestel and J. J. Uhl, Jr · 1977
Earlier work this paper cites.
Interpolation Theory, Function Spaces, Differential Operators , North-Holland Publishing Co., Amsterdam, 1978
H. Triebel · 1978
Earlier work this paper cites.
Remarks on inequalities for large deviation probabilities
I. F. Pinelis and A. I. Sakhanenko · 1986
Earlier work this paper cites.
Entropy, Compactness and the Approximation of Operators , Cambridge University Press, Cambridge, 1990
B. Carl and I. Stephani · 1990
Earlier work this paper cites.
Probability Theory , De Gruyter, Berlin, 1996
H. Bauer · 1996
Earlier work this paper cites.
Function Spaces, Entropy Numbers, Differential Operators , Cambridge University Press, Cambridge, 1996
D. E. Edmunds and H. Triebel · 1996
Earlier work this paper cites.
A Distribution-free Theory of Nonparametric Regression , Springer, New York, 2002
L. Györfi, M. Kohler, A. Krzyżak, and H. Walk · 2002
Earlier work this paper cites.
Sobolev Spaces , Elsevier/Academic Press, Amsterdam, second edition, 2003
R. A. Adams and J. J. F. Fournier · 2003
Earlier work this paper cites.
Shannon sampling and function reconstruction from point values
S. Smale and D.-X. Zhou · 2004
Earlier work this paper cites.
Shannon sampling II: Connections to learning theory
S. Smale and D.-X. Zhou · 2005
Earlier work this paper cites.
Discretization error analysis for Tikhonov regularization
E. De Vito, L. Rosasco, and A. Caponnetto · 2006
Earlier work this paper cites.
On regularization algorithms in learning theory
F. Bauer, S. Pereverzev, and L. Rosasco · 2007
Cited alongside, same era.
Optimal rates for the regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2007
Cited alongside, same era.
Learning theory estimates via integral operators and their approximations
S. Smale and D.-X. Zhou · 2007
Cited alongside, same era.
Support Vector Machines , Springer, New York, 2008
I. Steinwart and A. Christmann · 2008
Cited alongside, same era.
Optimal rates for regularized least squares regression
I. Steinwart, D. Hush, and C. Scovel · 2009
Cited alongside, same era.
Introduction to Nonparametric Estimation , Springer, New York, 2009
A. B. Tsybakov · 2009
Cited alongside, same era.
Optimal rates for regularization of statistical inverse learning problems
G. Blanchard and N. Mücke · 2017
Closest in time.
Kernel ridge vs. principal component regression: Minimax bounds and the qualification of regularization operators
L. H. Dicker, D. P. Foster, and D. Hsu · 2017
Closest in time.
Sobolev norm learning rates for regularized least-squares algorithm
S. Fischer and I. Steinwart · 2017
Closest in time.
On some extensions of Bernstein’s inequality for self-adjoint operators
S. Minsker · 2017
Closest in time.
Learning rates for kernel-based expectile regression
M. Farooq and I. Steinwart · 2018
Closest in time.
Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces
J. Lin, A. Rudi, L. Rosasco, and V. Cevher · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Regularization in kernel learning
S. Mendelson and J. Neeman · 2010
Cited alongside, same era.
Mercer’s theorem on general domains: On the interaction between measures, kernels, and RKHSs
I. Steinwart and C. Scovel · 2012
Cited alongside, same era.
Optimal regression rates for SVMs using Gaussian kernels
M. Eberts and I. Steinwart · 2013
Cited alongside, same era.
Less is more: Nyström computational regularization
A. Rudi, R. Camoriano, and L. Rosasco · 2015
Cited alongside, same era.
An introduction to matrix concentration inequalities
J. A. Tropp · 2015
Cited alongside, same era.
Model selection for regularized least-squares algorithm in learning theory
E. De Vito, A. Caponnetto, and L. Rosasco
Cited in the paper.
Closest in time.
Parallelizing spectrally regularized kernel algorithms
N. Mücke and G. Blanchard · 2018
Closest in time.
Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes
L. Pillaud-Vivien, A. Rudi, and F. Bach · 2018
Closest in time.
Reducing training time by efficient localized kernel regression
N. Mücke · 2019
Closest in time.
Beating SGD saturation with tail-averaging and minibatching
N. Mücke, G. Neu, and L. Rosasco · 2019
Closest in time.