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Algorithm improved for selecting top candidates with predictions

By PulseAugur Editorial · [1 sources] ·

Researchers have improved upon the Learned Kleinberg algorithm for the multiple-choice secretary problem, which aims to select up to k candidates to maximize their total value when candidates arrive in a random order. The enhanced algorithm achieves a competitive ratio of at least \includegraphics[width=0.08\textwidth]{max}0,1-\includegraphics[width=0.08\textwidth]{min}\{32/\sqrt{k},4\eps\}}, removing a logarithmic factor from previous analyses. This improvement means that with exact predictions, the algorithm can achieve the optimal offline result regardless of the arrival order. AI

IMPACT Improves theoretical performance for selection algorithms with predictive capabilities.

RANK_REASON Academic paper detailing algorithmic improvements. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Algorithm improved for selecting top candidates with predictions

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Academic paper detailing algorithmic improvements. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jing Yuan, Qi Cai, Xin Gao, Shaojie Tang ·

    Multiple-Choice Secretary Problem with Predictions

    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 indivi…

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