Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression

31 Dec 2017 · Catoni Olivier, Giulini Ilaria ·

This paper is focused on dimension-free PAC-Bayesian bounds, under weak polynomial moment assumptions, allowing for heavy tailed sample distributions. It covers the estimation of the mean of a vector or a matrix, with applications to least squares linear regression. Special efforts are devoted to the estimation of Gram matrices, due to their prominent role in high-dimension data analysis.

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Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression

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