arXiv:2611.14651v1 Announce Type: cross
Abstract: We study the multiple-choice secretary problem, in which candidates arrive in uniformly random order and the goal is to select at most $k$ candidates to maximize their total value. We consider a setting where predictions of individual candidate values are available before any candidate arrives. Fujii and Yoshida's Learned Kleinberg algorithm achieves competitive loss $O(\min\{\log k/\sqrt{k},\eps\})$, where $k$ is the selection capacity and $\eps$ is the maximum multiplicative prediction error. We remove the logarithmic factor through a sharper analysis of the same switching algorithm with a retuned threshold. The resulting competitive ratio is at least $\max\{0,1-\min\{32/\sqrt{k},4\eps\}\}$. In particular, exact predictions ($\eps=0$) achieve the offline optimum on every arrival order.
Abstract: We study the multiple-choice secretary problem, in which candidates arrive in uniformly random order and the goal is to select at most $k$ candidates to maximize their total value. We consider a setting where predictions of individual candidate values are available before any candidate arrives. Fujii and Yoshida's Learned Kleinberg algorithm achieves competitive loss $O(\min\{\log k/\sqrt{k},\eps\})$, where $k$ is the selection capacity and $\eps$ is the maximum multiplicative prediction error. We remove the logarithmic factor through a sharper analysis of the same switching algorithm with a retuned threshold. The resulting competitive ratio is at least $\max\{0,1-\min\{32/\sqrt{k},4\eps\}\}$. In particular, exact predictions ($\eps=0$) achieve the offline optimum on every arrival order.
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