Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems

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.

Authors

• Bo Sun
• Russell Lee
• Mohammad Hajiesmaili University of Massachusetts Amherst
• Adam Wierman
• Danny Tsang

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