Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs

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Arindam Banerjee, Sheng Chen, Vidyashankar Sivakumar ;
Proceedings of The 28th Conference on Learning Theory, PMLR 40:1752-1755, 2015.

Abstract

The restricted eigenvalue (RE) condition characterizes the sample complexity of accurate recovery in the context of high-dimensional estimators such as Lasso and Dantzig selector (Bickel et al., 2009). Recent work has shown that random design matrices drawn from any thin-tailed (sub-Gaussian) distributions satisfy the RE condition with high probability, when the number of samples scale as the square of the Gaussian width of the restricted set (Banerjee et al., 2014; Tropp, 2015). We pose the equivalent question for heavy-tailed distributions: Given a random design matrix drawn from a heavy-tailed distribution satisfying the smallball property (Mendelson, 2015), does the design matrix satisfy the RE condition with the same order of sample complexity as sub-Gaussian distributions? An answer to the question will guide the design of highdimensional estimators for heavy tailed problems.

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