eCP: Informative uncertainty quantification via Equivariantized Conformal Prediction with pre-trained models
This provides more informative uncertainty quantification for applications like pedestrian trajectory prediction, though it appears incremental as it builds on existing conformal prediction methods.
The paper addresses the problem of uninformative uncertainty regions in conformal prediction during long horizon missions by incorporating geometric information through group-averaging of pre-trained models, resulting in provably contracted non-conformity scores and sharper prediction sets, especially at high confidence levels.
We study the effect of group symmetrization of pre-trained models on conformal prediction (CP), a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, CP uncertainty regions can grow significantly in long horizon missions, rendering the statistical guarantees uninformative. To that end, we propose infusing CP with geometric information via group-averaging of the pretrained predictor to distribute the non-conformity mass across the orbits. Each sample now is treated as a representative of an orbit, thus uncertainty can be mitigated by other samples entangled to it via the orbit inducing elements of the symmetry group. Our approach provably yields contracted non-conformity scores in increasing convex order, implying improved exponential-tail bounds and sharper conformal prediction sets in expectation, especially at high confidence levels. We then propose an experimental design to test these theoretical claims in pedestrian trajectory prediction.