Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High dimensions

ArXi:2603.10721v1 Announce Type: cross In this paper, we investigate the learning-augmented $k$-median clustering problem, which aims to improve the performance of traditional clustering algorithms by preprocessing the point set with a predictor of error rate $\alpha \in [0,1)$. This preprocessing step assigns potential labels to the points before clustering. We