# Active Linear Regression for ℓp Norms and Beyond

arXiv: https://arxiv.org/abs/2111.04888 · HTML: https://mlsanity.com/paper/2111.04888/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [On Socially Fair Low-Rank Approximation and Column Subset Selection](https://mlsanity.com/paper/2412.06063/) (2024)
- [On Robust Cross Domain Alignment](https://mlsanity.com/paper/2412.15861/) (2024)
- [Active Learning with Low-Rank Structure for Data Selection](https://mlsanity.com/paper/2606.16045/) (2026)

## Abstract (arXiv)

We study active sampling algorithms for linear regression, which aim to query only a few entries of a target vector b∈mathbb Rn and output a near minimizer to minx∈mathbb Rd |Ax-b|, for a design matrix A∈mathbb Rn × d and loss |·|. For p norm regression for any 0<p<∞, we give an algorithm based on Lewis weight sampling outputting a (1+ε)-approximate solution using just tilde O(d/ε2) queries to b for p∈(0,1), tildeO(d/ε) queries for 1<p<2, and tildeO(dp/2/εp) queries for 2<p<∞. For 0<p<2, our bounds are optimal up to log factors, settling the query complexity for this range. For 2<p<∞, our dependence on d is optimal, while our dependence on ε is off by at most ε, up to log factors. Our result resolves an open question of [CD21], who gave near optimal bounds for the 1 norm, but required d2/ε2 samples for ℓp regression with 1<p<2, and gave no bounds for 2<p<∞ or 0<p<1. We also give the first total sensitivity bound of O(d^max1,p/2log2n) for loss functions of degree p polynomial growth, improving a result of [TMF20]. By combining this with our techniques for ℓp regression, we obtain an active regression algorithm making tilde O(d^1+max1,p/2/poly(ε)) queries for such loss functions, including the Tukey and Huber losses, answering another question of [CD21]. For the Huber loss, we further improve our bound to tilde O(d4-2√2/poly(ε)) samples. Our sensitivity bounds also have many applications, including Orlicz norm subspace embeddings, robust subspace approximation, and dimension reduction for smoothed p-norms. Finally, our active sampling results give the first sublinear time algorithms for Kronecker product regression under every p norm.