Conformal Prediction with Learned Features

arXiv:2404.17487v1 Announce Type: new
Abstract: In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with full conditional coverage guarantees. A wealth of research has considered relaxations of full conditional guarantees, relying on some predefined uncertainty structures. Departing from this line of thinking, we propose Partition Learning Conformal Prediction (PLCP), a framework to improve conditional validity of prediction sets through learning uncertainty-guided features …

abstract arxiv construct coverage cs.ai cs.lg features focus paper prediction prior research stat.ml type uncertainty wealth work