# Design of c-Optimal Experiments for High dimensional Linear Models

arXiv: https://arxiv.org/abs/2010.12580 · HTML: https://mlsanity.com/paper/2010.12580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

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

- [PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits](https://mlsanity.com/paper/2210.15345/) (2022)
- [Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits](https://mlsanity.com/paper/2402.11156/) (2024)

## Abstract (arXiv)

We study random designs that minimize the asymptotic variance of a de-biased lasso estimator when a large pool of unlabeled data is available but measuring the corresponding responses is costly. The optimal sampling distribution arises as the solution of a semidefinite program. The improvements in efficiency that result from these optimal designs are demonstrated via simulation experiments.