Jon C. Ergun

2 accepted papers

2025

Learning-Augmented Hierarchical Clustering

Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou

ICML 2025poster

Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong…

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2022

Learning-Augmented $k$-means Clustering

Jon C. Ergun, Zhili Feng, Sandeep Silwal, David Woodruff, Samson Zhou

ICLR 2022spotlight

$k$-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the $k$-means problem on worst-case inputs. To overcome this barrier, we consider a scenario where ``advice'' is provided to help…

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