Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees. To produce valid intervals which are also adaptive to the difficulty of each instance, a common approach is to compute normalized nonconformity scores on a separate calibration set. Self-supervised learning has been effectively utilized in many domains to learn general representations for downstream predictors. However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored. In this work, we investigate how self-supervised pretext tasks can improve the quality of the conformal regressors, specifically by improving the adaptability of conformal intervals. We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.

翻译：共形预测是一种强大的无分布假设的不确定性量化工具，能够建立具有有限样本保证的有效预测区间。为生成既有效又能适应每个实例难度的预测区间，常见方法是在独立校准集上计算归一化的非符合性分数。自监督学习已在多个领域被有效用于学习面向下游预测器的通用表征，但其在模型预训练和表征学习之外的应用方式尚未得到充分探索。本研究探究自监督前置任务如何提升共形回归器的质量，具体通过改进共形区间的自适应性。我们在现有预测模型基础上，利用自监督前置任务训练辅助模型，并将自监督误差作为额外特征用于估计非符合性分数。通过合成数据与真实数据的实验，我们实证证明了附加信息在共形预测区间效率（宽度）、赤字和冗余方面的优势。