Conformal prediction uses past experience to determine precise levels of confidence in new
predictions. Given an error probability ε, together with a method that makes a prediction ˆy of …

We develop methods for forming prediction sets in an online setting where the data
generating distribution is allowed to vary over time in an unknown fashion. Our framework …

Enzyme engineering plays a central role in developing efficient biocatalysts for
biotechnology, biomedicine, and life sciences. Apart from classical rational design and …

Although the existing machine learning-based intrusion detection systems in the Internet of
Things (IoT) usually perform well in static environments, they struggle to preserve their …

Evaluating treatment effect heterogeneity widely informs treatment decision making. At the
moment, much emphasis is placed on the estimation of the conditional average treatment …

We consider the problem of forming prediction sets in an online setting where the
distribution generating the data is allowed to vary over time. Previous approaches to this …

Unreliable predictions can occur when an artificial intelligence (AI) system is presented with
data it has not been exposed to during training. We demonstrate the use of conformal …

We propose a model-free framework for sensitivity analysis of individual treatment effects
(ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of
neuroimaging with strong potential to be used in practice. In this context, assessment of …