Leveraged volume sampling for linear regression

NeurIPS 2018 Michał DerezińskiManfred K. WarmuthDaniel Hsu

Suppose an $n \times d$ design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a small number $k \ll n$ of the responses, and then produce a weight vector whose sum of squares loss over all points is at most $1+\epsilon$ times the minimum... (read more)

PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper