论文标题
一种新型的深度学习方法，用于一步形式预测近似
A novel Deep Learning approach for one-step Conformal Prediction approximation
论文作者
论文摘要
对于现实世界中的问题，尤其是在高风险环境中，具有可衡量的置信度的深度学习预测越来越多。共形预测（CP）框架是一种多功能解决方案，可确保给定最小约束的最大错误率。在本文中，我们提出了一种新型的保形损耗函数，该功能在一个步骤中近似传统上两步的CP方法。通过评估和惩罚与严格的预期CP输出分布的偏差，深度学习模型可以学习输入数据与保形P值之间的直接关系。我们进行了全面的经验评估，以显示我们在五个基准数据集中对七个二元和多级预测任务的新损失函数的竞争力。在同一数据集上，我们的方法与汇总的共形预测（ACP）相比，达到了高达86％的训练时间，同时保持了可比的近似有效性和预测效率。
Deep Learning predictions with measurable confidence are increasingly desirable for real-world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is a versatile solution that guarantees a maximum error rate given minimal constraints. In this paper, we propose a novel conformal loss function that approximates the traditionally two-step CP approach in a single step. By evaluating and penalising deviations from the stringent expected CP output distribution, a Deep Learning model may learn the direct relationship between the input data and the conformal p-values. We carry out a comprehensive empirical evaluation to show our novel loss function's competitiveness for seven binary and multi-class prediction tasks on five benchmark datasets. On the same datasets, our approach achieves significant training time reductions up to 86% compared to Aggregated Conformal Prediction (ACP), while maintaining comparable approximate validity and predictive efficiency.
