10 citations · 26 across the 11 of their papers we have counts for
11 papers
Wasserstein Proximal Coordinate Gradient Algorithms
Rentian Yao, Xiaohui Chen, Yun Yang
Motivated by approximation Bayesian computation using mean-field variational approximation and the computation of equilibrium in multi-species systems with cross-interaction, this…
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
Yubo Zhuang, Xiaohui Chen, Yun Yang +1
-means clustering is a widely used machine learning method for identifying patterns in large datasets. Recently, semidefinite programming (SDP) relaxations have been proposed fo…
Wasserstein -means for clustering probability distributions
Yubo Zhuang, Xiaohui Chen, Yun Yang
Clustering is an important exploratory data analysis technique to group objects based on their similarity. The widely used -means clustering method relies on some notion of dist…
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Yubo Zhuang, Xiaohui Chen, Yun Yang
Clustering is a widely deployed unsupervised learning tool. Model-based clustering is a flexible framework to tackle data heterogeneity when the clusters have different shapes. Lik…
Mean-Field Nonparametric Estimation of Interacting Particle Systems
Rentian Yao, Xiaohui Chen, Yun Yang
This paper concerns the nonparametric estimation problem of the distribution-state dependent drift vector field in an interacting -particle system. Observing single-trajectory d…
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for -means Clustering
Yubo Zhuang, Xiaohui Chen, Yun Yang
Semidefinite programming (SDP) is a powerful tool for tackling a wide range of computationally hard problems such as clustering. Despite the high accuracy, semidefinite programs ar…