A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm as a black-box, and redesign online algorithms to take advantage of ML predictions. In this paper, we ask the complementary question: can we redesign ML algorithms to provide better predictions for online algorithms? We explore this question in the context of the classic rent-or-buy problem, and show that incorporating optimization benchmarks in ML loss functions leads to significantly better performance, while maintaining a worst-case adversarial result when the advice is completely wrong. We support this finding both through theoretical bounds and numerical simulations.

翻译：最近一流的研究方针在设计在线算法时纳入了ML建议,以提高其在典型情况下的性能。 这些文件将ML算法视为黑盒,并重新设计在线算法以利用ML预测。 在本文中,我们问一个补充问题:我们能否重新设计ML算法,为在线算法提供更好的预测?我们从典型的租购问题的角度来探讨这一问题,并表明将优化基准纳入ML损失功能会大大改善性能,同时在建议完全错误时保持最坏的对抗性结果。我们通过理论界限和数字模拟支持这一结果。