@report {1616617,
	title = {Leverage Score Sampling for Faster Accelerated Regression and Empirical Risk Minimization},
	year = {2020},
	institution = {ArXiv Report},
	address = {ALT},
	abstract = {Given a matrix A∈ℝn{\texttimes}d and a vector b∈ℝd, we show how to compute an ϵ-approximate solution to the regression problem minx∈ℝd12{\textbardbl}Ax-b{\textbardbl}22 in time Õ ((n+d.κsum-------√).s.logϵ-1) where κsum=tr(A⊤A)/λmin(ATA) and s is the maximum number of non-zero entries in a row of A. Our algorithm improves upon the previous best running time of Õ ((n+n.κsum-------√).s.logϵ-1). We achieve our result through a careful combination of leverage score sampling techniques, proximal point methods, and accelerated coordinate descent. Our method not only matches the performance of previous methods, but further improves whenever leverage scores of rows are small (up to polylogarithmic factors). We also provide a non-linear generalization of these results that improves the running time for solving a broader class of ERM problems.},
	url = {https://arxiv.org/abs/1711.08426},
	author = {Naman Agarwal and Sham Kakade and Rahul Kidambi and Yin Tat Lee and Praneeth Netrapalli and Aaron Sidford}
}
