thresholding

This paper studies the fairness problem of thresholded subgroup underdiagnosis in long-tailed chest X-ray classification, demonstrating that rare-label fairness depends jointly on the finding, subgroup, and operating threshold, not on label frequency or ranking metrics alone.

This paper adapts classical class imbalance techniques to Prior-Data Fitted Networks (PFNs) for tabular classification, finding that thresholding and downsampling perform well due to PFNs' calibration and limited-data capabilities.