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cs.LG2026
GEM-KMeans: Memory-Efficient and Accurate Clustering on Massive Scale with GPU Optimization
Peng Xu, Nihar Koganti, Volodymyr Kindratenko +1
Memory-efficient scaling on clustering problems without sacrificing statistical accuracy is of central interest for large-scale data analysis and machine learning problems. Nonnega…
stat.ML2026
Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence
Peng Xu, Changbo Zhu, Young-Heon Kim +1
This paper is concerned with learning principal variations of random probability measures on under the Wasserstein geometry. We introduce a new dynamical formulation…
math.OC2026
A Unified Approach for Computing Wasserstein Barycenters of Discrete and Continuous Measures
Peng Xu, Changbo Zhu, Xiaohui Chen
Computing the unregularized Wasserstein barycenter for measure-valued data is a challenging optimization task. Recent algorithms have been tailored to either discrete measures as p…