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arXiv:2209.07396 (stat)
[Submitted on 15 Sep 2022 (v1), last revised 22 Nov 2025 (this version, v3)]

Title:Towards Healing the Blindness of Score Matching

Authors:Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
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Abstract:Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using these for multi-modal distributions. In this work, we discuss the blindness problem and propose a new family of divergences that can mitigate the blindness problem. We illustrate our proposed divergence in the context of density estimation and report improved performance compared to traditional approaches.
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:2209.07396 [stat.ML]
  (or arXiv:2209.07396v3 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2209.07396

Submission history

From: Mingtian Zhang [view email]
[v1] Thu, 15 Sep 2022 15:56:42 UTC (387 KB)
[v2] Sat, 15 Oct 2022 09:18:01 UTC (387 KB)
[v3] Sat, 22 Nov 2025 12:23:05 UTC (242 KB)
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