Publications [#357948] of Rong Ge

Papers Published

1. Anand, K; Ge, R, Customizing ML predictions for online algorithms, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-1 (January, 2020), pp. 280-290, ISBN 9781713821120
   (last updated on 2024/04/23)

   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 blackbox, 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 signifcantly better performance, while maintaining a worst-case adversarial result when the advice is completely wrong. We support this fnding both through theoretical bounds and numerical simulations.