Abstract:Learning of continuous exponential family distributions with unbounded support remains an important area of research for both theory and applications in high-dimensional statistics. In recent years, score matching has become a widely used method for learning exponential families with continuous variables due to its computational ease when compared against maximum likelihood estimation. However, theoretical understanding of the statistical properties of score matching is still lacking. In this work, we provide a non-asymptotic sample complexity analysis for learning the structure of exponential families of polynomials with score matching. The derived sample bounds show a polynomial dependence on the model dimension. These bounds are the first of its kind, as all prior work has shown only asymptotic bounds on the sample complexity.
| Comments: | 22 pages |
| Subjects: | Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML) |
| Report number: | LA-UR-26-21103 |
| Cite as: | arXiv:2605.14168 [cs.LG] |
| (or arXiv:2605.14168v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14168 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Devin Smedira [view email]
[v1]
Wed, 13 May 2026 22:48:18 UTC (31 KB)