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Found 4 papers, 4 papers with code
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions
Mutual information quantifies the dependence between two random variables and remains invariant under diffeomorphisms.
Beyond Normal: On the Evaluation of Mutual Information Estimators
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.
Bayesian Quantification with Black-Box Estimators
Understanding how different classes are distributed in an unlabeled data set is an important challenge for the calibration of probabilistic classifiers and uncertainty quantification.
Unsupervised Recalibration
Unsupervised recalibration (URC) is a general way to improve the accuracy of an already trained probabilistic classification or regression model upon encountering new data while deployed in the field.
