动态环境中自适应选择模型，提升预测集效率与可靠性
Multi-model Ensemble Conformal Prediction in Dynamic Environments
- 动态切换多个候选模型生成预测集
- 在真实与合成数据上均更高效且覆盖有效
- 适合应对未知分布漂移的场景
置信预测是一种不确定性量化方法，能为未见样本构建预测集，确保真实标签以预定覆盖率被包含。自适应置信预测用于应对动态环境中的数据分布漂移问题。然而，不同学习模型生成的预测集效率差异显著。使用单一固定模型在未知分布漂移的动态环境中难以持续保持最优性能。为此，本文提出一种新型自适应置信预测框架，根据实时情况从多个候选模型中动态选择用于生成预测集的模型。所提算法在所有时间区间内实现强自适应后悔率，同时保证覆盖有效性。在真实和合成数据集上的实验表明，该方法始终生成更高效的预测集，且覆盖有效，优于现有替代方法。
原文摘要 · Abstract (English)
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.
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