A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms are designed to take advantage of a possibly-imperfect prediction of some aspect of the problem. While much work has focused on using predictions to improve competitive ratios, running times, or other performance measures, less effort has been devoted to the question of how to obtain the predictions themselves, especially in the critical online setting. We introduce a general design approach for algorithms that learn predictors: (1) identify a functional dependence of the performance measure on the prediction quality, and (2) apply techniques from online learning to learn predictors against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling. In the first and last settings we improve upon existing learning-theoretic results by deriving online results, obtaining better or more general statistical guarantees, and utilizing a much simpler analysis, while in the second and fourth we provide the first learning-theoretic guarantees.

翻译：在算法设计方面,一个新兴的范式是算法和预测法的领域,在这种算法中,算法的设计是为了利用对问题的某些方面可能不完全的预测。虽然许多工作的重点是利用预测来改进竞争比率、运行时间或其他性能衡量尺度,但是在如何获得预测本身的问题上,特别是在关键的在线环境里,投入的努力较少。我们为学习预测者的算法采用一种一般的设计方法:(1) 确定业绩计量在功能上对预测质量的依赖性,(2) 应用网上学习的技术来学习预测者对抗对抗敌对情况、调和稳健一致的取舍和获得新的统计保证。我们通过分析双部分匹配、页迁移、滑雪和工作时间安排的方法,展示了我们得出学习算法的方法的有效性。在第一个和最后一个环境中,我们改进了现有的学习理论结果,通过在线结果、获得更好或更普遍的统计保证,并利用更简单的分析,同时在第二个和第四个环境中,我们提供了第一个学习理论保证。