First sampling algorithms with O(ε) additive error guarantees for local and global silhouette estimation in metric k-clustering, using O(nk ε^{-2} ln(nk/δ)) distances, plus constant-round distributed MapReduce/MPC versions.
A new partitioning around medoids algorithm , year =
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First strongly Bayes-consistent algorithm for metric-valued regression with unbounded loss in the agnostic setting, based on metric medoids and semi-stable compression.
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Metric-valued regression
First strongly Bayes-consistent algorithm for metric-valued regression with unbounded loss in the agnostic setting, based on metric medoids and semi-stable compression.