The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the extent to which predictions can be trusted -- while existing approaches often require users to specify an aggregate trust level, modern machine learning models can provide estimates of prediction-level uncertainty. In this paper, we propose calibration as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies: the ski rental and online job scheduling problems. For ski rental, we design an algorithm that achieves near-optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty quantification. For job scheduling, we demonstrate that using a calibrated predictor leads to significant performance improvements over existing methods. Evaluations on real-world data validate our theoretical findings, highlighting the practical impact of calibration for algorithms with predictions.

翻译：带预测的算法领域将机器学习建议融入在线算法设计中，以提升实际性能。核心考量在于预测的可信程度——现有方法通常要求用户指定全局信任级别，而现代机器学习模型能够提供预测层面的不确定性估计。本文提出将校准作为弥合这一差距的原则性实用工具，通过两个案例研究（滑雪租赁与在线作业调度问题）论证校准建议的优势。针对滑雪租赁问题，我们设计的算法实现了接近最优的预测相关性能，并证明在高方差环境下，校准建议比其它不确定性量化方法能提供更有效的指导。针对作业调度问题，我们证明采用校准预测器相比现有方法能带来显著的性能提升。真实数据上的评估验证了我们的理论发现，突显了校准对带预测算法的实际价值。