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This paper leverages machine learned predictions to design online algorithmsfor the k-max and k-min search problems. Our algorithms can achieveperformances competitive with the offline algorithm in hindsight when thepredictions are accurate (i.e., consistency) and also provide worst-caseguarantees when the predictions are arbitrarily wrong (i.e., robustness).Further, we show that our algorithms have attained the Pareto-optimal trade-offbetween consistency and robustness, where no other algorithms for k-max ork-min search can improve on the consistency for a given robustness. Todemonstrate the performance of our algorithms, we evaluate them in experimentsof buying and selling Bitcoin.

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