Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fail. In this paper we focus on online problems; prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box (to get the predictions it was trained for). In contrast, in this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge. In particular we allow the predictor to learn as it receives larger parts of the input, with the ultimate goal of designing online learning algorithms specifically tailored for the algorithmic task at hand. Adopting this perspective, we focus on a number of fundamental problems, including caching and scheduling, which have been well-studied in the black-box setting. For each of the problems we consider, we introduce new algorithms that take advantage of explicit learning algorithms which we carefully design towards optimizing the overall performance. We demonstrate the potential of our approach by deriving performance bounds which improve over those established in previous work.

翻译：近期算法设计方面的进展展示了如何利用机器学习模型从过去和当前数据中获得的预测结果。这些方法在预测准确时能提升性能，同时在预测失败时通过提供最坏情况保证确保鲁棒性。本文聚焦在线问题；此前该领域的研究主要基于将预测器在历史数据上预训练后作为黑箱使用（以获取其训练目标对应的预测结果）。与之不同，本研究打开预测器"黑箱"，将其引发的学习问题融入算法挑战本身。具体而言，我们允许预测器在接收更多输入数据的过程中进行持续学习，最终目标是为具体算法任务定制在线学习算法。基于这一视角，我们聚焦缓存调度等若干已在黑箱设定中得到充分研究的基础问题。针对每个问题，我们提出结合显式学习算法的新方案，这些学习算法经过精心设计以优化整体性能。通过推导优于已有工作的性能界，我们展示了该方法的潜力。