Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage is essential for fairness across different collections of data points and for providing finer UQ guarantees. Parameter-free optimization is crucial for robustness to adversarial and unknown data shifts. We propose a parameter-free algorithm for group-conditional OCP and demonstrate that it achieves the best group-conditional coverage guarantees. We evaluate our algorithm on synthetic and real-world data, demonstrating that our method not only improves the reliability of existing parameter-free OCP methods but also provides prediction intervals that are comparable in size to well-tuned group-conditional approaches. By unifying group-conditional coverage with parameter-free online algorithms, our work lays a foundation for fair and robust uncertainty quantification in shifting environments.

翻译：不确定性量化对于机器学习预测器在数据分布随时间变化（即数据可能不可交换）的现实场景中的部署至关重要。在线共形预测方法通过牺牲以下任一条件来解决该问题：（i）分组误差控制或（ii）与学习率无关的实现。分组条件覆盖对于确保不同数据点集合的公平性以及提供更精细的不确定性量化保证至关重要。无参数优化对于抵御对抗性和未知数据漂移具有关键作用。我们提出了一种用于分组条件在线共形预测的无参数算法，并证明其能达到最优的分组条件覆盖保证。我们通过合成数据和真实数据对所提算法进行评估，结果表明该方法不仅提升了现有无参数在线共形预测方法的可靠性，而且其生成的预测区间规模与经过良好调参的分组条件方法相当。通过将分组条件覆盖与无参数在线算法相结合，我们的工作为漂移环境下的公平且鲁棒的不确定性量化奠定了基础。