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To interpret uncertainty estimates from differentiable probabilistic models,recent work has proposed generating Counterfactual Latent UncertaintyExplanations (CLUEs). However, for a single input, such approaches could outputa variety of explanations due to the lack of constraints placed on theexplanation. Here we augment the original CLUE approach, to provide what wecall $\delta$-CLUE. CLUE indicates $\it{one}$ way to change an input, whileremaining on the data manifold, such that the model becomes more confidentabout its prediction. We instead return a $\it{set}$ of plausible CLUEs:multiple, diverse inputs that are within a $\delta$ ball of the original inputin latent space, all yielding confident predictions.

{\delta}-CLUE: Diverse Sets of Explanations for Uncertainty Estimates