• Bernoulli
  • Volume 25, Number 4A (2019), 3016-3040.

Localized Gaussian width of $M$-convex hulls with applications to Lasso and convex aggregation

Pierre C. Bellec

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Upper and lower bounds are derived for the Gaussian mean width of a convex hull of $M$ points intersected with a Euclidean ball of a given radius. The upper bound holds for any collection of extreme points bounded in Euclidean norm. The upper bound and the lower bound match up to a multiplicative constant whenever the extreme points satisfy a one sided Restricted Isometry Property.

An appealing aspect of the upper bound is that no assumption on the covariance structure of the extreme points is needed. This aspect is especially useful to study regression problems with anisotropic design distributions. We provide applications of this bound to the Lasso estimator in fixed-design regression, the Empirical Risk Minimizer in the anisotropic persistence problem, and the convex aggregation problem in density estimation.

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Bernoulli, Volume 25, Number 4A (2019), 3016-3040.

Received: November 2017
Revised: June 2018
First available in Project Euclid: 13 September 2019

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anisotropic design convex aggregation convex hull Gaussian mean width Lasso localized Gaussian width


Bellec, Pierre C. Localized Gaussian width of $M$-convex hulls with applications to Lasso and convex aggregation. Bernoulli 25 (2019), no. 4A, 3016--3040. doi:10.3150/18-BEJ1078.

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