Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty Applications.

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Statistical Uncertainty Quantification for Expensive Black-Box Models

Feb 2, 2024 · This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, ...

Statistical Uncertainty Quantification for Expensive Black-Box Models

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Abstract: This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, which arise frequently ...

Statistical Uncertainty Quantification for Expensive Black-Box Models

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This tutorial reviews methodologies for quantifying statistical uncertainty in computationally expensive black-box models, which arise frequently in data-driven ...

A review of uncertainty quantification in deep learning - ScienceDirect.com

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The transparency, fairness and reliability of the methods can be improved by explaining black-box machine learning models. Model robustness and user trust ...

Machine Learning for Uncertainty Quantification: Trusting the Black Box

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Statistically Optimal Uncertainty Quantification for Expensive Black ...

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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 ...

Uncertainty Quantification for Black Box Models

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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 ...

CISPA researchers test new method for uncertainty quantification in ...

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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.

Uncertainty Quantification for Black-box Large Language Models

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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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