SQ Lower Bounds for Learning Mixtures of Linear Classifiers

Ilias Diakonikolas, Daniel Kane, Yuxin Sun

Keywords: mixtures models, linear classifier, Statistical Query model, spherical designs

TL;DR: We give nearly tight SQ lower bounds for learning mixtures of linear classifiers under Gaussian covariates.

Abstract:

We study the problem of learning mixtures of linear classifiers under Gaussian covariates.
Given sample access to a mixture of $r$ distributions on $\mathbb{R}^n$ of the form $(\mathbf{x},y_{\ell})$, $\ell \in [r]$,
where $\mathbf{x}\sim\mathcal{N}(0,\mathbf{I}_n)$ and

$y_\ell=\mathrm{sign}(\langle\mathbf{v}_{\ell},\mathbf{x}\rangle)$

for an unknown unit vector $\mathbf{v}_{\ell}$,

the goal is to learn the underlying distribution in total variation distance. Our main result is a Statistical Query (SQ) lower bound suggesting that known algorithms for this problem are essentially best possible,
even for the special case of uniform mixtures.
In particular, we show that the complexity of any SQ algorithm for the problem is $n^{\mathrm{poly}(1/\Delta) \log(r)}$,
where $\Delta$ is a lower bound on the pairwise $\ell_2$-separation between the $\mathbf{v}_{\ell}$'s.
The key technical ingredient underlying our result is a new construction of spherical designs on the unit sphere that may be of independent interest.

Submission Number: 9620