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 …

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes
with more evidence. While existing work focuses on using the variance of the Bayesian …

We present a new distribution-free conformal prediction algorithm for sequential data (eg,
time series), called the sequential predictive conformal inference (SPCI). We specifically …

Spiking neural networks (SNNs) process time-series data via internal event-driven neural
dynamics. The energy consumption of an SNN depends on the number of spikes exchanged …

Sparse deep learning has become a popular technique for improving the performance of
deep neural networks in areas such as uncertainty quantification, variable selection, and …

We introduce a method for online conformal prediction with decaying step sizes. Like
previous methods, ours possesses a retrospective guarantee of coverage for arbitrary …

When used in complex engineered systems, such as communication networks, artificial
intelligence (AI) models should be not only as accurate as possible, but also well calibrated …

Conformal prediction (CP) has been a popular method for uncertainty quantification
because it is distribution-free, model-agnostic, and theoretically sound. For forecasting …

Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework,
reliably provides valid predictive inference for black-box models. CP constructs prediction …

This paper presents a new conformal method for generating simultaneous forecasting bands
guaranteed to cover the entire path of a new random trajectory with sufficiently high …