Finite Sample Bounds for Learning with Score Matching
Authors: Devin Smedira, Abhijith Jayakumar, Sidhant Misra et al.
Summary
arXiv:2605. 14168v1 Announce Type: cross 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.
Relevance
Read next because Finite Sample Bounds for Learning with Score Matching overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: under, compare, model. Source: arxiv stat.ML (Machine Learning).
Abstract
arXiv:2605.14168v1 Announce Type: cross 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.