Customizing ML Predictions for Online Algorithms
This work addresses the challenge of enhancing ML advice for online algorithms, offering a novel approach that could benefit algorithm designers, though it is incremental as it builds on existing research in ML-augmented online algorithms.
The paper tackles the problem of improving ML predictions for online algorithms by redesigning ML algorithms to incorporate optimization benchmarks, specifically in the rent-or-buy problem, resulting in significantly better performance while maintaining worst-case guarantees.
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.