Adaptive Conformal Prediction for Complex Inputs

Music City Center 

Deep models are powerful tools that are increasingly used in analyses of complex/multimodal data. Many attempts at producing estimates with associated uncertainty are ad hoc at best, with only a relative notion of uncertainty. Statistically valid uncertainty quantification (UQ) to accompany deep model predictions is necessary to gain traction for using these methods on high risk applications where expensive or important decisions hinge on the results. Conformal prediction promises statistically valid intervals (assuming exchangeable data), but UQ does not vary according to local difficulty of the problem. We propose an extension of conformal prediction to computer vision to provide UQ for a deep model with complex/multimodal inputs and explore methods to provide local adaptivity beyond simple continuous inputs for an image input application.

Conformal prediction

Uncertainty quantification

Deep learning

Computer vision 

Section on Statistics in Defense and National Security