Research Questions Standard Definition of Epistemic Uncertainty in Machine Learning
A new arXiv paper challenges the standard machine learning taxonomy that defines epistemic uncertainty as reducible through additional data, proving the definition and its standard measure are inconsistent. The authors propose a three-part framework distinguishing aleatoric, sample-reducible epistemic, and mechanism-reducible epistemic uncertainty. The findings have implications for how machine learning systems estimate and reduce uncertainty in predictions.
Researchers have published a theoretical paper on arXiv arguing that the conventional understanding of epistemic uncertainty in machine learning contains a fundamental inconsistency. The standard definition treats epistemic uncertainty as the portion of prediction error that can be eliminated by collecting more training data, typically measured using mutual information. However, the authors demonstrate through explicit construction that this measure can assign all uncertainty to the epistemic category while remaining irreducible regardless of additional data collected. They propose instead that reducibility depends on the pair of uncertainty and acquisition class, leading to a three-part taxonomy: aleatoric uncertainty, sample-reducible epistemic uncertainty, and mechanism-reducible epistemic uncertainty. The paper includes theoretical analysis showing that in-distribution data never reduces mechanism-irreducible uncertainty and typically increases it, along with experimental validation through finite-sample falsification tests and seed-swept experiments.
What's missing
The paper's own limitations and open questions are not detailed in the abstract provided. Additionally, the practical implications for specific machine learning applications and how practitioners should adjust their approaches based on these findings are not discussed in the available excerpt.
What different sources said
- arXiv stat.MLCenter
Epistemic Uncertainty Is Not the Reducible Kind
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