Pareto-Optimal Learning-Augmented Algorithms for Online k-Search Problems

This paper leverages machine learned predictions to design online algorithms for the k-max and k-min search problems. Our algorithms can achieve performances competitive with the offline algorithm in hindsight when the predictions are accurate (i.e., consistency) and also provide worst-case guarantees when the predictions are arbitrarily wrong (i.e., robustness). Further, we show that our algorithms have attained the Pareto-optimal trade-off between consistency and robustness, where no other algorithms for k-max or k-min search can improve on the consistency for a given robustness. To demonstrate the performance of our algorithms, we evaluate them in experiments of buying and selling Bitcoin.