probability-estimation

A unified framework for estimating the probability of data-dependent events, covering missing mass, adaptive data analysis, and test-error estimation, with new hybrid Good–Turing style estimators and bounds based on VC dimension and mutual information.

This paper proposes CORD, a post-fit adapter for post-hoc calibration that repairs probability vectors to preserve original top-1 predictions while maintaining calibration quality, achieving zero TPCR and improved metrics on datasets like CIFAR and ImageNet.

This paper introduces GA-AMLS, a rare-event Monte Carlo method adapted to language model activation spaces, and SPB Loss, a proper scoring rule for asymmetric penalties, demonstrating improved estimation of rare harmful outputs.