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Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model
We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of~$m$ i.i.d.\ sub-Gaussian samples and communicate a limited number of bits to a central server. While \cite{rahmani2
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