Feb 2, 2024 · This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, ...
Abstract: This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, which arise frequently ...
This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, which arise frequently in data-driven ...
The transparency, fairness and reliability of the methods can be improved by explaining black-box machine learning models. Model robustness and user trust ...
Duration: 32:53
Posted: Jun 6, 2022
Posted: Jun 6, 2022
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Our developments build on a new bridge of the classical notion of uniformly most accurate unbiasedness with batching and resampling, by viewing model runs as ...
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Uncertainty accounting requires both quantifying uncertainty for models, or components of models, and propagating that uncertainty through other aspects of the ...
Jul 10, 2020 · We aim to tailor existing methods from the emulation and calibration literature to such problems, providing full-field predictions of the model ...
Dec 15, 2023 · For a machine learning algorithm to be trustworthy, the end user needs to know how confident the model is for every single prediction.
Mar 11, 2024 · This paper presents a work on measuring prediction uncertainty in large language models without accessing the prediction probability or logits; ...
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