Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. However, the efficiency of prediction sets varies depending on the learning model used. Employing a single fixed model may not consistently offer the best performance in dynamic environments with unknown data distribution shifts. To address this issue, we introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected on the fly from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage. Experiments on real and synthetic datasets corroborate that the proposed approach consistently yields more efficient prediction sets while maintaining valid coverage, outperforming alternative methods.

本研究解决了在动态环境下数据分布变化导致的预测集效率不稳定的问题。我们提出了一种新颖的自适应一致预测框架，通过实时从多个候选模型中选择生成预测集的模型，显著提升了预测集的效率，同时保持了有效的覆盖率。实验结果表明，该方法在真实和合成数据集上均优于其他替代方法。 

动态环境中的多模型集合一致预测