6 citations · 6 across the 3 of their papers we have counts for
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stat.ME2026
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…
stat.ME2024
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…