# Communication-efficient sparse regression: a one-shot approach

arXiv: https://arxiv.org/abs/1503.04337 · HTML: https://mlsanity.com/paper/1503.04337/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2015.

## Built on by (most cited first, 2 of 2)

- [Distributed Sparse Regression via Penalization](https://mlsanity.com/paper/2111.06530/) (2021)
- [Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression](https://mlsanity.com/paper/2306.08432/) (2023)

## Abstract (arXiv)

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average "debiased" or "desparsified" lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach to generalized linear models.