Pareto-Optimal Learning-Augmented Algorithms for Online Conversion ProblemsDownload PDF

21 May 2021, 20:43 (modified: 22 Jan 2022, 02:08)NeurIPS 2021 PosterReaders: Everyone
Keywords: learning-augmented algorithm, robustness, consistency, Pareto-optimality, online conversion problem
TL;DR: Optimal robustness-consistency trade-offs for learning-augmented algorithms for online conversion problems
Abstract: 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.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
12 Replies