Measuring Stochastic Data Complexity with Boltzmann Influence Functions
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
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 …