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