In this work we study the problem of using machine-learned predictions to improve the performance of online algorithms. We consider two classical problems, ski rental and non-clairvoyant job scheduling, and obtain new online algorithms that use predictions to make their decisions. These algorithms are oblivious to the performance of the predictor, improve with better predictions, but do not degrade much if the predictions are poor.

翻译：本研究探讨如何利用机器学习预测提升在线算法的性能。我们针对两个经典问题——滑雪板租赁问题与非先知作业调度问题，提出了基于预测进行决策的新型在线算法。这些算法不依赖于预测器的性能表现，能够随着预测质量的提升而改进，同时在预测效果不佳时性能下降幅度有限。