QUEST measures uncertainty via the Lebesgue volume of highest-density regions of a distribution's support, evaluated at robustness parameter alpha, and claims to satisfy UQ axioms while outperforming variance and differential entropy on selective prediction tasks.
arXiv preprint arXiv:2006.11590 , year=
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Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
Semantic entropy improves uncertainty estimation in natural language generation by incorporating semantic equivalences, outperforming standard entropy baselines on predicting model accuracy for question answering.
citing papers explorer
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On the QUEST for Uncertainty Quantification via Highest Density Regions
QUEST measures uncertainty via the Lebesgue volume of highest-density regions of a distribution's support, evaluated at robustness parameter alpha, and claims to satisfy UQ axioms while outperforming variance and differential entropy on selective prediction tasks.
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Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
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Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
Semantic entropy improves uncertainty estimation in natural language generation by incorporating semantic equivalences, outperforming standard entropy baselines on predicting model accuracy for question answering.