Web: http://arxiv.org/abs/2209.07396

Sept. 16, 2022, 1:12 a.m. | Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol

cs.LG updates on arXiv.org arxiv.org

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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