Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical
problem of estimating a bounded mean. Our approach generalizes and improves on the …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

Trustworthy deployment of ML models requires a proper measure of uncertainty, especially
in safety-critical applications. We focus on uncertainty quantification (UQ) for classification …

Whenever a binary classifier is used to provide decision support, it typically provides both a
label prediction and a confidence value. Then, the decision maker is supposed to use the …

A multiclass classifier is said to be top-label calibrated if the reported probability for the
predicted class--the top-label--is calibrated, conditioned on the top-label. This conditioning …

Many selection processes such as finding patients qualifying for a medical trial or retrieval
pipelines in search engines consist of multiple stages, where an initial screening stage …

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which
combines the Platt scaling technique with online logistic regression. We demonstrate that …

We propose causal isotonic calibration, a novel nonparametric method for calibrating
predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a …

Identifying how much a model ${\widehat {p}} _ {\theta}(Y| X) $ knows about the stochastic
real-world process $ p (Y| X) $ it was trained on is important to ensure it avoids producing …