Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture,
healthcare, entertainment, and other industries. Most of these systems are developed with …

Recent advances in Transformer-based large language models (LLMs) have led to
significant performance improvements across many tasks. These gains come with a drastic …

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

Image-to-image regression is an important learning task, used frequently in biological
imaging. Current algorithms, however, do not generally offer statistical guarantees that …

We extend conformal prediction to control the expected value of any monotone loss function.
The algorithm generalizes split conformal prediction together with its coverage guarantee …

We study the problem of uncertainty quantification via prediction sets, in an online setting
where the data distribution may vary arbitrarily over time. Recent work develops online …

We propose a novel approach to conformal prediction for generative language models
(LMs). Standard conformal prediction produces prediction sets--in place of single predictions …

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

Abstract Machine learning has advanced dramatically, narrowing the accuracy gap to
humans in multimodal tasks like visual question answering (VQA). However, while humans …

Uncertainty quantification is essential for the reliable deployment of machine learning
models to high-stakes application domains. Uncertainty quantification is all the more …