55 citations · 62 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
Scalable Second-order Riemannian Optimization for -means Clustering
Peng Xu, Chun-Ying Hou, Xiaohui Chen +1
Clustering is a hard discrete optimization problem. Nonconvex approaches such as low-rank semidefinite programming (SDP) have recently demonstrated promising statistical and local…
Embedding Empirical Distributions for Computing Optimal Transport Maps
Mingchen Jiang, Peng Xu, Xichen Ye +3
Distributional data have become increasingly prominent in modern signal processing, highlighting the necessity of computing optimal transport (OT) maps across multiple probability…
Holographic deep thermalization for secure and efficient quantum random state generation
Bingzhi Zhang, Peng Xu, Xiaohui Chen +1
Randomness is a cornerstone of science, underpinning fields such as statistics, information theory, dynamical systems, and thermodynamics. In quantum science, quantum randomness, e…
Generative quantum machine learning via denoising diffusion probabilistic models
Bingzhi Zhang, Peng Xu, Xiaohui Chen +1
Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently ga…