<ul><li>This paper proposes an extension of the Hausdorff distance to spaces equipped with asymmetric distance measures, specifically focusing on the family of Bregman divergences.</li><li>The Bregman-Hausdorff divergence is used to compare probabilistic predictions produced by different machine learning models trained using the relative entropy loss.</li><li>The proposed algorithms are efficient even for large inputs with high dimensions.</li><li>The paper also provides a survey on Bregman geometry and computational geometry algorithms relevant to machine learning.</li></ul>

Bregman-Hausdorff divergence: strengthening the connections between computational geometry and machine learning

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