High Dimensional Linear Regression using Lattice Basis Reduction

Wednesday, March 14, 2018 - 4:30pm to 5:00pm

Event Calendar Category

LIDS & Stats Tea

Speaker Name

Ilias Zadik



Building and Room Number

LIDS Lounge


In this talk, we focus on the high dimensional linear regression problem where the goal is to efficiently recover an unknown vector β* from n noisy linear observations Y = Xβ* + W ∈ ℝn, for known X ∈ ℝn × p and unknown W ∈ ℝn. Unlike most of the literature on this model we make no sparsity assumption on β*. Instead we adopt a regularization based on assuming that the underlying vectors β* have rational entries with the same denominator Q ∈ ℤ>0. We call this Q-rationality assumption.

We propose a new polynomial-time algorithm for this task which is based on the seminal Lenstra-Lenstra-Lovasz (LLL) lattice basis reduction algorithm. We establish that under the Q-rationality assumption, our algorithm recovers exactly the vector β* for a large class of distributions for the iid entries of X and non-zero noise W. We prove that it is successful under small noise, even when the learner has access to only one observation (n = 1).

Joint work with David Gamarnik.