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 from the calibration data. We implement PLCP
efficiently with alternating gradient descent, utilizing off-the-shelf machine
learning models. We further analyze PLCP theoretically and provide conditional
guarantees for infinite and finite sample sizes. Finally, our experimental
results over four real-world and synthetic datasets show the superior
performance of PLCP compared to state-of-the-art methods in terms of coverage
and length in both classification and regression scenarios.

本文关注条件保证的合拟合预测问题，并提出了采用从校准数据中学习的不确定性引导特征来改进预测集的条件有效性的分区学习合拟合预测（PLCP）框架。我们在理论上分析了 PLCP，并对无限和有限样本大小提供了条件保证。最后，我们在四个真实世界和人工合成数据集上的实验证明了 PLCP 在分类和回归场景中相对于最先进方法在覆盖率和长度方面的卓越表现。