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arXiv · 2406.02745

Measuring Stochastic Data Complexity with Boltzmann Influence Functions

Nathan Ng·Roger Grosse·Marzyeh Ghassemi
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Abstract

Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts. A minimum description length approach to this problem uses the predictive normalized maximum likelihood (pNML) distribution, which considers every possible label for a data point, and decreases confidence in a prediction if other labels are also consistent with the model and training data. In this work we propose IF-COMP, a scalable and efficient approximation of the pNML distribution that linearizes the model with a temperature-scaled Boltzmann influence function. IF-COMP can be used to produce well-calibrated predictions on test points as well as measure complexity in both labelled and unlabelled settings. We experimentally validate IF-COMP on uncertainty calibration, mislabel detection, and OOD detection tasks, where it consistently matches or beats strong baseline methods.

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Nathan Ng, Roger Grosse, Marzyeh Ghassemi. 2024-06-04. Measuring Stochastic Data Complexity with Boltzmann Influence Functions. https://arxiv.org/abs/2406.02745

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