We study the efficient estimation of predictive confidence intervals for black-box predictors
when the common data exchangeability (eg, iid) assumption is violated due to potentially …

Morden deep ensembles technique achieves strong uncertainty estimation performance by
going through multiple forward passes with different models. This is at the price of a high …

A fundamental goal of evaluating the performance of a clinical model is to ensure it performs
well across a diverse intended patient population. A primary challenge is that the data used …

Transfer learning aims to transfer knowledge or information from a source domain to a
relevant target domain. In this paper, we understand transfer learning from the perspectives …

Training-conditional coverage guarantees in conformal prediction concern the concentration
of the error distribution, conditional on the training data, below some nominal level. The …

Machine learning algorithms have grown in sophistication over the years and are
increasingly deployed for real-life applications. However, when using machine learning …

We propose JAWA-FCS, which uses higher-order influence functions to approximate
predictive intervals of the (previous) jackknife+ weighted for feedback covariate shift for …

We develop a method to generate prediction sets with a guaranteed coverage rate that is
robust to corruptions in the training data, such as missing or noisy variables. Our approach …

Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex
statistical models. Their validity hinges on reliable calibration data, which may not be readily …

As machine learning (ML) gains widespread adoption, practitioners are increasingly seeking
means to quantify and control the risk these systems incur. This challenge is especially …