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Robust scatter estimation is a fundamental task in statistics.
A new measure of rank correlation
Maurice G Kendall · 1938
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Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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Ordinal measures of association
William H Kruskal · 1958
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Robust estimation of a location parameter
Peter J Huber · 1964
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A robust version of the probability ratio test
Peter J Huber · 1965
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Elicitation of personal probabilities and expectations
Leonard J Savage · 1971
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Mathematics and the picturing of data
John W Tukey · 1975
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Robust m m -estimators of multivariate location and scatter
Ricardo Antonio Maronna · 1976
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Monte carlo methods of inference for implicit statistical models
Peter J Diggle and Richard J Gratton · 1984
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Robust Regression and Outlier Detection
Annick M Leroy and Peter J Rousseeuw · 1987
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A distribution-free m m -estimator of multivariate scatter
David E Tyler · 1987
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On the method of bounded differences
Colin McDiarmid · 1989
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The asymptotics of Rousseeuw’s minimum volume ellipsoid estimator
Laurie Davies · 1992
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Concentration of measure and isoperimetric inequalities in product spaces
Michel Talagrand · 1995
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Yoav Freund and Robert E. Schapire · 1996
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Weak convergence and empirical processes
Aad W van der Vaart and Jon A Wellner · 1996
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On Tyler’s M-functional of scatter in high dimension
Lutz Dümbgen · 1998
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Computing location depth and regression depth in higher dimensions
Peter J Rousseeuw and Anja Struyf · 1998
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Regression depth
Peter J Rousseeuw and Mia Hubert · 1999
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Regression depth and center points
Nina Amenta, Marshall Bern, David Eppstein, and S-H Teng · 2000
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Yijun Zuo and Robert Serfling · 2000
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Jian Zhang · 2002
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Generalization error bounds for bayesian mixture algorithms
Ron Meir and Tong Zhang · 2003
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An optimal randomized algorithm for maximum tukey depth
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Loss functions for binary class probability estimation and classification: Structure and applications
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Aapo Hyvärinen · 2005
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f-gan: Training generative neural samplers using variational divergence minimization
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Generative models and model criticism via optimized maximum mean discrepancy
Dougal J Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2016
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Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas
Marten Wegkamp and Yue Zhao · 2016
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Marčenko–Pastur law for Tyler’s M-estimator
Teng Zhang, Xiuyuan Cheng, and Amit Singer · 2016
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Strictly proper scoring rules, prediction, and estimation
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Understanding the difficulty of training deep feedforward neural networks
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2012
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Convergence of stochastic processes
David Pollard · 2012
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
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Computationally efficient robust estimation of sparse functionals
Simon S Du, Sivaraman Balakrishnan, and Aarti Singh · 2017
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Symmetric Multivariate and Related Distributions
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Understanding gans: the lqg setting
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How well can generative adversarial networks (gan) learn densities: A nonparametric view
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Least squares generative adversarial networks
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On the discrimination-generalization tradeoff in gans
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2017
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Approximability of discriminators implies diversity in gans
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Rho-estimators revisited: General theory and applications
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Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Robust covariance and scatter matrix estimation under huber’s contamination model
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Robust estimation and generative adversarial nets
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Likelihood-free inference via classification
Michael U Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander · 2018
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Halfspace depths for scatter, concentration and shape matrices
Davy Paindaveine and Germain Van Bever · 2018
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Deconstructing generative adversarial networks
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