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Most supervised machine learning tasks are subject to irreducible prediction errors.
The generalization of student’s ratio
H. Hotelling · 1931
Earlier work this paper cites.
A class of statistics with asymptotically normal distribution
W. Hoeffding · 1948
Earlier work this paper cites.
On the identifiability of finite mixtures
S. J. Yakowitz and J. D. Spragins · 1968
Earlier work this paper cites.
Reliability of subjective probability forecasts of precipitation and temperature
A. H. Murphy and R. L. Winkler · 1977
Earlier work this paper cites.
A tree-structured approach to nonparametric multiple regression
J. H. Friedman · 1979
Earlier work this paper cites.
Approximation Theorems of Mathematical Statistics
R. J. Serfling (ed.) · 1980
Earlier work this paper cites.
The comparison and evaluation of forecasters
M. H. DeGroot and S. E. Fienberg · 1983
Earlier work this paper cites.
Multidimensional additive spline approximation
J. H. Friedman, E. Grosse, and W. Stuetzle · 1983
Earlier work this paper cites.
Harmonic Analysis on Semigroups
C. Berg, J. P. R. Christensen, and P. Ressel · 1984
Earlier work this paper cites.
Real analysis and probability
R. M. Dudley · 1989
Earlier work this paper cites.
On a formula for the l 2 l^{2} Wasserstein metric between measures on Euclidean and Hilbert spaces
M. Gelbrich · 1990
Earlier work this paper cites.
Multivariate adaptive regression splines
J. H. Friedman · 1991
Earlier work this paper cites.
On the bootstrap of U U and V V statistics
M. A. Arcones and E. Giné · 1992
Earlier work this paper cites.
Quadratic forms in random variables: Theory and applications , volume 126
A. M. Mathai and S. B. Provost · 1992
Earlier work this paper cites.
Continuous univariate distributions: Vol. 1
N. L. Johnson, S. Kotz, and N. Balakrishnan · 1994
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
A. Müller · 1997
Earlier work this paper cites.
A multivariate generalization of the power exponential family of distributions
E. Gómez, M. A. Gómez-Viilegas, and J. M. Marín · 1998
Earlier work this paper cites.
Asymptotic Statistics
A. W. van der Vaart · 1998
Earlier work this paper cites.
Probabilities for SV Machines , pp. 61–73
J. Platt · 2000
Earlier work this paper cites.
Reducing multiclass to binary by coupling probability estimates
B. Zadrozny · 2002
Earlier work this paper cites.
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
K. Fukumizu, F. R. Bach, and M. I. Jordan · 2004
Earlier work this paper cites.
Calibrated interpolated confidence intervals for population quantiles
Y. H. S. Ho and S. M. S. Lee · 2005
Earlier work this paper cites.
On learning vector-valued functions
C. A. Micchelli and M. Pontil · 2005
Earlier work this paper cites.
Universal Kernels
C. A. Micchelli, Y. Xu, and H. Zhang · 2006
Cited alongside, same era.
Calibration methods for estimating quantiles
M. Rueda, S. Martínez-Puertas, H. Martínez-Puertas, and A. Arcos · 2006
Cited alongside, same era.
Increasing the reliability of reliability diagrams
J. Bröcker and L. A. Smith · 2007
Cited alongside, same era.
A kernel method for the two-sample-problem
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola · 2007
Cited alongside, same era.
Kernel measures of conditional dependence
K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf · 2008
Cited alongside, same era.
Multivariate exponential power distributions as mixtures of normal distributions with Bayesian applications
E. Gómez-Sánchez-Manzano, M. A. Gómez-Villegas, and J. M. Marín · 2008
Cited alongside, same era.
Conditional generative moment-matching networks
Y. Ren, J. Zhu, J. Li, and Y. Luo · 2016
Later among the works it cites.
Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics
M. Taillardat, O. Mestre, M. Zamo, and P. Naveau · 2016
Later among the works it cites.
On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
Later among the works it cites.
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
M. Kull, T. Silva Filho, and P. Flach · 2017
Later among the works it cites.
Image denoising with generalized Gaussian mixture model patch priors
C. Deledalle, S. Parameswaran, and T. Q. Nguyen · 2018
Later among the works it cites.
Flux: Elegant machine learning with Julia
M. Innes · 2018
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J. Bröcker · 2009
Cited alongside, same era.
A fast, consistent kernel two-sample test
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Cited alongside, same era.
Hilbert space embeddings of conditional distributions with applications to dynamical systems
L. Song, J. Huang, A. J. Smola, and K. Fukumizu · 2009
Cited alongside, same era.
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B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. G. Lanckriet · 2009
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
Later among the works it cites.
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Later among the works it cites.
Accurate uncertainties for deep learning using calibrated regression
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Trainable calibration measures for neural networks from kernel mean embeddings
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Characteristic and universal tensor product kernels
Z. Szabó and B. K. Sriperumbudur · 2018
Later among the works it cites.
Optimal transport for Gaussian mixture models
Y. Chen, T. T. Georgiou, and A. Tannenbaum · 2019
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M. Kull, M. Perello Nieto, M. Kängsepp, T. Silva Filho, H. Song, and P. Flach · 2019
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