This paper introduces a novel K-step active subsampling algorithm designed for estimating an optimal individualized threshold in high-dimensional data under measurement constraints. The algorithm iteratively selects the most informative data points for labeling to minimize discrepancies between predicted and actual outcomes, particularly in scenarios where labeling is costly. Theoretical analysis indicates a sharp phase transition phenomenon related to the smoothness of the conditional density of the data. AI
RANK_REASON The cluster contains an academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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