  Non-Gaussian Component Analysis (NGCA) is the following distribution learning
problem: Given i.i.d. samples from a distribution on $\mathbb{R}^d$ that is
non-gaussian in a hidden direction $v$ and an independent standard Gaussian in
the orthogonal directions, the goal is to approximate the hidden direction $v$.
Prior work \cite{DKS17-sq} provided formal evidence for the existence of an
information-computation tradeoff for NGCA under appropriate moment-matching
conditions on the univariate non-gaussian distribution $A$. The latter result
does not apply when the distribution $A$ is discrete. A natural question is
whether information-computation tradeoffs persist in this setting. In this
paper, we answer this question in the negative by obtaining a sample and
computationally efficient algorithm for NGCA in the regime that $A$ is discrete
or nearly discrete, in a well-defined technical sense. The key tool leveraged
in our algorithm is the LLL method \cite{LLL82} for lattice basis reduction.
