---
res:
  bibo_abstract:
  - This paper provides statistical sample complexity bounds for score-matching and
    its applications in causal discovery. We demonstrate that accurate estimation
    of the score function is achievable by training a standard deep ReLU neural network
    using stochastic gradient descent. We establish bounds on the error rate of recovering
    causal relationships using the score-matching-based causal discovery method of
    Rolland et al. [2022], assuming a sufficiently good estimation of the score function.
    Finally, we analyze the upper bound of score-matching estimation within the score-based
    generative modeling, which has been applied for causal discovery but is also of
    independent interest within the domain of generative models.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Zhenyu
      foaf_name: Zhu, Zhenyu
      foaf_surname: Zhu
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Locatello, Francesco
      foaf_surname: Locatello
      foaf_workInfoHomepage: http://www.librecat.org/personId=26cfd52f-2483-11ee-8040-88983bcc06d4
    orcid: 0000-0002-4850-0683
  - foaf_Person:
      foaf_givenName: Volkan
      foaf_name: Cevher, Volkan
      foaf_surname: Cevher
  bibo_doi: 10.48550/arXiv.2310.18123
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_title: 'Sample complexity bounds for score-matching: Causal discovery and generative
    modeling@'
...