6 citations · 6 across the 3 of their papers we have counts for
5 papers
Byzantine-tolerant distributed learning of finite mixture models under partial corruptions
Yimei Zhang, Jiahua Chen, Xiaozhou Wang +2
Finite mixture models characterize heterogeneous populations and are increasingly fitted to distributed data using split-and-conquer procedures that aggregate local mixture estimat…
Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS
Tao Wang, Mengyu Li, Geduo Zeng +2
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for radiance field rendering, but it typically requires millions of redundant Gaussian primitives, overwhelming mem…
Byzantine-tolerant distributed learning of finite mixture models
Qiong Zhang, Yan Shuo Tan, Jiahua Chen
Traditional statistical methods need to be updated to work with modern distributed data storage paradigms. A common approach is the split-and-conquer framework, which involves lear…
Minimum Wasserstein Distance Estimator under Finite Location-scale Mixtures
Qiong Zhang, Jiahua Chen
When a population exhibits heterogeneity, we often model it via a finite mixture: decompose it into several different but homogeneous subpopulations. Contemporary practice favors l…
Gaussian Mixture Reduction with Composite Transportation Divergence
Qiong Zhang, Archer Gong Zhang, Jiahua Chen
Gaussian mixtures are widely used for approximating density functions in various applications such as density estimation, belief propagation, and Bayesian filtering. These applicat…