Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …

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 …

Until recently, conventional biochemical staining had the undisputed status as well-
established benchmark for most biomedical problems related to clinical diagnostics …

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 …

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

Deep neural networks are powerful tools to detect hidden patterns in data and leverage
them to make predictions, but they are not designed to understand uncertainty and estimate …

Uncertainty quantification is crucial for the deployment of image restoration models in safety-
critical domains, like autonomous driving and biological imaging. To date, methods for …

Score-based generative modeling, informally referred to as diffusion models, continue to
grow in popularity across several important domains and tasks. While they provide high …