The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

As large language models continue to be widely developed, robust uncertainty
quantification techniques will become crucial for their safe deployment in high-stakes …

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty
quantification in machine learning since it can be easily applied as a post-processing step to …

Automated decision support systems promise to help human experts solve multiclass
classification tasks more efficiently and accurately. However, existing systems typically …

We study the statistical properties of learning to defer (L2D) to multiple experts. In particular,
we address the open problems of deriving a consistent surrogate loss, confidence …

Conditional validity and length efficiency are two crucial aspects of conformal prediction
(CP). Achieving conditional validity ensures accurate uncertainty quantification for data …

As deep neural networks are more commonly deployed in high-stakes domains, their black-
box nature makes uncertainty quantification challenging. We investigate the effects of …

In response to everyday queries, humans explicitly signal uncertainty and offer alternative
answers when they are unsure. Machine learning models that output calibrated prediction …

Decision support systems for classification tasks are predominantly designed to predict the
value of the ground truth labels. However, since their predictions are not perfect, these …

Individual human decision-makers may benefit from different forms of support to improve
decision outcomes. However, a key question is which form of support will lead to accurate …