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Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
Zhenyu Zhu · Francesco Locatello · Volkan Cevher

Thu Dec 14 03:00 PM -- 05:00 PM (PST) @ Great Hall & Hall B1+B2 #1017

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.

Author Information

Zhenyu Zhu (EPFL)
Francesco Locatello (AWS)
Volkan Cevher (EPFL)

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