K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Recent advances in generative modeling have led to an increased interest in the study of
statistical divergences as means of model comparison. Commonly used evaluation …

Clustering is a long-standing important research problem, however, remains challenging
when handling large-scale image data from diverse sources. In this paper, we present a …

We propose a new dataset distillation algorithm using reparameterization and
convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art …

The use of Bayesian methods in large-scale data settings is attractive because of the rich
hierarchical models, uncertainty quantification, and prior specification they provide …

K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

This paper presents a novel accelerated exact k-means called as" Ball k-means" by using
the ball to describe each cluster, which focus on reducing the point-centroid distance …

Seeding-the task of finding initial cluster centers-is critical in obtaining high-quality
clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does …

We investigate coresets-succinct, small summaries of large data sets-so that solutions found
on the summary are provably competitive with solution found on the full data set. We provide …

This paper presents a novel accelerated exact-means called as “Ball-means” by using the
ball to describe each cluster, which focus on reducing the point-centroid distance …