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Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data.
Geometric Measure Theory
H. Federer · 1969
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On the uniform convergence of relative frequencies of events to their expectation
V. Vapnik and A. Chervonenkis · 1971
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Optimal rates of convergence for non-parametric estimators
C. J. Stone · 1980
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Optimal global rates of convergence for non-parametric estimators
C. J. Stone · 1982
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Local bandwidth selection for kernel estimates
J. G. Staniswalis · 1989
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Rate of convergence for the wild bootstrap in nonparametric regression
R. Cao-Abad · 1991
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Riemannian Geometry
M. do Carmo · 1992
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A review of the theory and estimation of fractal dimension
C. Cutler · 1993
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Rates of convergence of nearest neighbor estimation under arbitrary sampling
S. Kulkarni and S. Posner · 1995
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Every complete doubling metric space carries a doubling measure
J. Luukkainen and E. Saksman · 1998
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Nonlinear dimensionality reduction by locally linear embedding
S. Roweis and L. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
J. Tenebaum, V. de Silva, and J. Langford · 2000
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A Distribution Free Theory of Nonparametric Regression
L. Gyorfi, M. Kohler, A. Krzyzak, and H. Walk · 2002
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Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
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Nearest-neighbor searching and metric space dimensions
K. Clarkson · 2005
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Local polynomial regression on unknown manifolds
P. Bickel and B. Li · 2006
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Escaping the curse of dimensionality with a tree-based regressor
S. Kpotufe · 2009
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Fast, smooth, and adaptive regression in metric spaces
S. Kpotufe · 2009
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