Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of
predictive models. Despite the development of numerous recalibration algorithms, there is …

In a sequential regression setting, a decision-maker may be primarily concerned with
whether the future observation will increase or decrease compared to the current one, rather …

Estimates of causal parameters such as conditional average treatment effects and
conditional quantile treatment effects play an important role in real-world decision making …

Abstract Machine learning classifiers typically provide scores for the different classes. These
scores are supplementary to class predictions and may be crucial for downstream decision …

Lots of popular calibration methods in medical images focus on classification, but there are
few comparable studies on semantic segmentation. In polyp segmentation of medical …

Predicting the future has long been a fundamental aspiration of humankind. In the current
era of big data, the capacity to collect vast amounts of information presents a substantial …

We provide details on how we assess a sequence of distributional forecasts {ˆFt} T t= 1 and
parity probabilities {pt} T t= 1, given a test dataset Dtest={xt, yt} T t= 1. We assess …