NeurIPS Conference 2021 Conference Paper
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
- Bo Sun
- Russell Lee
- Mohammad Hajiesmaili
- Adam Wierman
- Danny Tsang
This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i. e. , consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i. e. , robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i. e. , no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.