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Workshop: NeurIPS 2022 Workshop on Score-Based Methods

Towards Healing the Blindness of Score Matching

Mingtian Zhang · Oscar Key · Peter Hayes · David Barber · Brooks Paige · Francois-Xavier Briol


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.

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