SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
<|reference_start|>SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions: We study the complexity of Non-Gaussian Component Analysis (NGCA) in the Statistical Query (SQ) model. Prior work developed a general methodology to prove SQ lower bounds for this task that have been applicable to a wide range of contexts. In particular, it was known that for any univariate distribution $A$ satisfying certain conditions, distinguishing between a standard multivariate Gaussian and a distribution that behaves like $A$ in a random hidden direction and like a standard Gaussian in the orthogonal complement, is SQ-hard. The required conditions were that (1) $A$ matches many low-order moments with the standard univariate Gaussian, and (2) the chi-squared norm of $A$ with respect to the standard Gaussian is finite. While the moment-matching condition is necessary for hardness, the chi-squared condition was only required for technical reasons. In this work, we establish that the latter condition is indeed not necessary. In particular, we prove near-optimal SQ lower bounds for NGCA under the moment-matching condition only. Our result naturally generalizes to the setting of a hidden subspace. Leveraging our general SQ lower bound, we obtain near-optimal SQ lower bounds for a range of concrete estimation tasks where existing techniques provide sub-optimal or even vacuous guarantees.<|reference_end|>
@article{diakonikolas2024sq,
    title={SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker
  Assumptions},
    author={Ilias Diakonikolas, Daniel Kane, Lisheng Ren and Yuxin Sun},
    journal={arXiv preprint arXiv:2403.04744},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04744},
    primaryClass={cs.LG cs.DS math.ST stat.ML stat.TH}
}