  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.
