Web: http://arxiv.org/abs/1609.07195

Sept. 15, 2022, 1:12 a.m. | Mahsa Taheri, Néhémy Lim, Johannes Lederer

stat.ML updates on arXiv.org arxiv.org

Modern technologies are generating ever-increasing amounts of data. Making
use of these data requires methods that are both statistically sound and
computationally efficient. Typically, the statistical and computational aspects
are treated separately. In this paper, we propose an approach to entangle these
two aspects in the context of regularized estimation. Applying our approach to
sparse and group-sparse regression, we show that it can improve on standard
pipelines both statistically and computationally.

applications arxiv computational general precision regression statistical theory

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