Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression

Mahsa Taheri; Néhémy Lim; Johannes Lederer

doi:10.1109/tit.2022.3203857

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arXiv · 1609.07195

Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression

Mahsa Taheri·Néhémy Lim·Johannes Lederer

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Abstract

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.

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Mahsa Taheri, Néhémy Lim, Johannes Lederer. 2016-09-23. Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression. https://doi.org/10.1109/tit.2022.3203857

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