Minimax rates for memory-bounded sparse linear regression
Proceedings of The 28th Conference on Learning Theory, PMLR 40:1564-1587, 2015.
We establish a minimax lower bound of Ω(\frackdBε) on the sample size needed to estimate parameters in a k-sparse linear regression of dimension d under memory restrictions to B bits, where εis the \ell_2 parameter error. When the covariance of the regressors is the identity matrix, we also provide an algorithm that uses \tildeO(B+k) bits and requires \tildeO(\frackdBε^2) observations to achieve error ε. Our lower bound also holds in the more general communication-bounded setting, where instead of a memory bound, at most B bits of information are allowed to be (adaptively) communicated about each sample.