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.
@article{arxiv.2205.08715,
title = {Customizing ML Predictions for Online Algorithms},
author = {Keerti Anand and Rong Ge and Debmalya Panigrahi},
journal= {arXiv preprint arXiv:2205.08715},
year = {2022}
}