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Found 4 papers, 2 papers with code
Sequential Kernelized Independence Testing
Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data.
Tracking the risk of a deployed model and detecting harmful distribution shifts
When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain -- but not all -- distribution shifts could result in significant performance degradation.
Distribution-free uncertainty quantification for classification under label shift
Piggybacking on recent progress in addressing label shift (for better prediction), we examine the right way to achieve UQ by reweighting the aforementioned conformal and calibration procedures whenever some unlabeled data from the target distribution is available.
Distribution-free binary classification: prediction sets, confidence intervals and calibration
We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data.
