thresholding

论文提出 Edit Discovery Optimization (EDO) 问题框架，用于在有限编辑器成本预算下选择性地将人机交互数据交给更强的编辑器（人类专家或更强模型）检查与修正，并提出在线算法 EDIT 在四个推理与事实问答基准上取得更优的成本-错误权衡。

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.