A short note about the learning-augmented secretary problem
<|reference_start|>A short note about the learning-augmented secretary problem: We consider the secretary problem through the lens of learning-augmented algorithms. As it is known that the best possible expected competitive ratio is $1/e$ in the classic setting without predictions, a natural goal is to design algorithms that are 1-consistent and $1/e$-robust. Unfortunately, [FY24] provided hardness constructions showing that such a goal is not attainable when the candidates' true values are allowed to scale with $n$. Here, we provide a simple and explicit alternative hardness construction showing that such a goal is not achievable even when the candidates' true values are constants that do not scale with $n$.<|reference_end|>
@article{choo2024a,
    title={A short note about the learning-augmented secretary problem},
    author={Davin Choo, Chun Kai Ling},
    journal={arXiv preprint arXiv:2410.06583},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06583},
    primaryClass={cs.DS}
}