Setting the entropic regularization of a balanced Sinkhorn plan to a constant times the mean closest-anchor cost of k-means anchors yields a cold-start active learning method that outperforms and unifies typicality-, coverage-, and diversity-based selectors.
Convergence of Entropic Schemes for Optimal Transport and Gradient Flows
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One Knob to Rule Them All: A Unified Optimal Transport View of Cold-Start Active Learning
Setting the entropic regularization of a balanced Sinkhorn plan to a constant times the mean closest-anchor cost of k-means anchors yields a cold-start active learning method that outperforms and unifies typicality-, coverage-, and diversity-based selectors.