Hierarchical Clustering is a popular unsupervised machine learning method with decades of
history and numerous applications. We initiate the study of differentially-private …

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for
training such dual encoders is an accurate estimation of gradients from the partition function …

We introduce TeraHAC, a (1+ ε)-approximate hierarchical agglomerative clustering (HAC)
algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to …

What is the widest community in which a person exercises a strong impact? Although
extensive attention has been devoted to searching communities containing given …

Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and
applied method for hierarchical clustering. Recent applications to massive datasets have …

We study graph spanners for point-set in the high-dimensional Euclidean space. On the one
hand, we prove that spanners with stretch<\sqrt {2} and subquadratic size are not possible …

We study graph spanners for point-set in the high-dimensional Euclidean space. On the one
hand, we prove that spanners with stretch \lt2 and subquadratic size are not possible, even if …

We introduce the ParClusterers Benchmark Suite (PCBS)--a collection of highly scalable
parallel graph clustering algorithms and benchmarking tools that streamline comparing …

We consider the problem of maintaining a hierarchical agglomerative clustering (HAC) in the
dynamic setting, when the input is subject to point insertions and deletions. We introduce …

We give an efficient algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering
(HAC), which computes a $ c $-approximate clustering in roughly $ n^{1+ O (1/c^ 2)} $ time …