On sample complexity for covariance estimation via the unadjusted Langevin algorithm

We establish sample complexity guarantees for estimating the covariance matrix of strongly log-concave smooth distributions using the unadjusted Langevin algorithm (ULA). We quantitatively compare our complexity estimates on single-chain ULA with embarrassingly parallel ULA and derive that the sample complexity of the single-chain approach is smaller than that of embarrassingly parallel ULA by a logarithmic factor in the dimension and the reciprocal of the prescribed precision, with the difference arising from effective bias reduction through burn-in. The key technical contribution is a concentration bound for the sample covariance matrix around its expectation, derived via a log-Sobolev inequality for the joint distribution of ULA iterates.

Source: arXiv:2601.21717v1 - http://arxiv.org/abs/2601.21717v1 PDF: https://arxiv.org/pdf/2601.21717v1 Original Link: http://arxiv.org/abs/2601.21717v1