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We consider the problem of mean estimation assuming only finite variance.
PAC-Bayes under potentially heavy tails
Holland, M. J. (2019a) · 1905
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Systems of frequency curves generated by methods of translation
Johnson, N. L. (1949) · 1949
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Handbook of Mathematical Functions With Formulas, Graphs, and Mathematical Tables
Abramowitz, M. and Stegun, I. A. (1964) · 1964
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Kendall’s Advanced Theory of Statistics Volume 1: Distribution Theory
Stuart, A. and Ord, J. K. (1994) · 1994
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Robust empirical mean estimators
Lerasle, M. and Oliveira, R. I. (2011) · 2011
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Challenging the empirical mean and empirical variance: a deviation study
Catoni, O. (2012) · 2012
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Computing the Kullback-Leibler divergence between two Weibull distributions
Bauckhage, C. (2013) · 2013
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Empirical risk minimization for heavy-tailed losses
Brownlees, C., Joly, E., and Lugosi, G. (2015) · 2015
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Sub-gaussian mean estimators
Devroye, L., Lerasle, M., Lugosi, G., and Oliveira, R. I. (2016) · 2016
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Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression
Catoni, O. and Giulini, I. (2017) · 2017
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Robust descent using smoothed multiplicative noise
Holland, M. J. (2019b)
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y., Su, L., and Xu, J. (2017) · 2017
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Robust regression using biased objectives
Holland, M. J. and Ikeda, K. (2017) · 2017
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Objective priors for the number of degrees of freedom of a multivariate
Villa, C. and Rubio, F. J. (2018) · 2018
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Better generalization with less data using robust gradient descent
Holland, M. J. and Ikeda, K. (2019) · 2019
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