Improved Online Conformal Prediction via Strongly Adaptive Online Learning

Understand

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 conformal prediction techniques that leverage regret minimization algorithms from the online learning literature to learn prediction sets with approximately valid coverage and small regret.
• However, standard regret minimization could be insufficient for handling changing environments, where performance guarantees may be desired not only over the full time horizon but also in all (sub-)intervals of time.
• We develop new online conformal prediction methods that minimize the strongly adaptive regret, which measures the worst-case regret over all intervals of a fixed length.