English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper provides statistical sample complexity bounds for score-matchingand its applications in causal discovery. We demonstrate that accurateestimation of the score function is achievable by training a standard deep ReLUneural network using stochastic gradient descent. We establish bounds on theerror rate of recovering causal relationships using the score-matching-basedcausal discovery method of Rolland et al. [2022], assuming a sufficiently goodestimation of the score function. Finally, we analyze the upper bound ofscore-matching estimation within the score-based generative modeling, which hasbeen applied for causal discovery but is also of independent interest withinthe domain of generative models.

Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling