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About this paper
Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin
SEMF introduces a supervised Expectation-Maximization framework that leverages latent variables to generate prediction intervals, allowing any machine learning model to quantify uncertainty. This model-agnostic approach produces notably narrower and more reliable prediction intervals, demonstrating strong performance, particularly with XGBoost on complex datasets.
Key insights
- The Expectation-Maximization algorithm can be effectively extended to a supervised context specifically for robust uncertainty quantification via latent variable modeling.
- Latent variable representations allow any base machine learning model, including those without inherent uncertainty estimation capabilities, to produce reliable prediction intervals.
- The framework's modular design, separating input encoding from output decoding through latent variables, provides a general, model-agnostic approach to uncertainty estimation.
Summary by alphaXiv.
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