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arXiv · 2205.08715

Customizing ML Predictions for Online Algorithms

Keerti Anand·Rong Ge·Debmalya Panigrahi
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Abstract

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.

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Keerti Anand, Rong Ge, Debmalya Panigrahi. 2022-05-18. Customizing ML Predictions for Online Algorithms. https://arxiv.org/abs/2205.08715

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