Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
CoRR(2024)
Abstract
This paper questions the effectiveness of a modern predictive uncertainty
quantification approach, called evidential deep learning (EDL), in which
a single neural network model is trained to learn a meta distribution over the
predictive distribution by minimizing a specific objective function. Despite
their perceived strong empirical performance on downstream tasks, a line of
recent studies by Bengs et al. identify limitations of the existing methods to
conclude their learned epistemic uncertainties are unreliable, e.g., in that
they are non-vanishing even with infinite data. Building on and sharpening such
analysis, we 1) provide a sharper understanding of the asymptotic behavior of a
wide class of EDL methods by unifying various objective functions; 2) reveal
that the EDL methods can be better interpreted as an out-of-distribution
detection algorithm based on energy-based-models; and 3) conduct extensive
ablation studies to better assess their empirical effectiveness with real-world
datasets. Through all these analyses, we conclude that even when EDL methods
are empirically effective on downstream tasks, this occurs despite their poor
uncertainty quantification capabilities. Our investigation suggests that
incorporating model uncertainty can help EDL methods faithfully quantify
uncertainties and further improve performance on representative downstream
tasks, albeit at the cost of additional computational complexity.
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