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<titleInfo><title>Sample complexity bounds for score-matching: Causal discovery and generative modeling</title></titleInfo>


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<name type="personal">
  <namePart type="given">Zhenyu</namePart>
  <namePart type="family">Zhu</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Francesco</namePart>
  <namePart type="family">Locatello</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">26cfd52f-2483-11ee-8040-88983bcc06d4</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-4850-0683</description></name>
<name type="personal">
  <namePart type="given">Volkan</namePart>
  <namePart type="family">Cevher</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>







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<abstract lang="eng">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.</abstract>

<originInfo><dateIssued encoding="w3cdtf">2023</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>arXiv</title></titleInfo>
  <identifier type="arXiv">2310.18123</identifier><identifier type="doi">10.48550/arXiv.2310.18123</identifier>
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<ieee>Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching: Causal discovery and generative modeling,” &lt;i&gt;arXiv&lt;/i&gt;. .</ieee>
<chicago>Zhu, Zhenyu, Francesco Locatello, and Volkan Cevher. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” &lt;i&gt;ArXiv&lt;/i&gt;, n.d. &lt;a href=&quot;https://doi.org/10.48550/arXiv.2310.18123&quot;&gt;https://doi.org/10.48550/arXiv.2310.18123&lt;/a&gt;.</chicago>
<apa>Zhu, Z., Locatello, F., &amp;#38; Cevher, V. (n.d.). Sample complexity bounds for score-matching: Causal discovery and generative modeling. &lt;i&gt;arXiv&lt;/i&gt;. &lt;a href=&quot;https://doi.org/10.48550/arXiv.2310.18123&quot;&gt;https://doi.org/10.48550/arXiv.2310.18123&lt;/a&gt;</apa>
<ista>Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv, 2310.18123.</ista>
<short>Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).</short>
<ama>Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. &lt;i&gt;arXiv&lt;/i&gt;. doi:&lt;a href=&quot;https://doi.org/10.48550/arXiv.2310.18123&quot;&gt;10.48550/arXiv.2310.18123&lt;/a&gt;</ama>
<mla>Zhu, Zhenyu, et al. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.” &lt;i&gt;ArXiv&lt;/i&gt;, 2310.18123, doi:&lt;a href=&quot;https://doi.org/10.48550/arXiv.2310.18123&quot;&gt;10.48550/arXiv.2310.18123&lt;/a&gt;.</mla>
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