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• W2891030779 abstract "In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure, while spending a small budget on communication." @default.
• W2891030779 created "2018-09-27" @default.
• W2891030779 creator A5026979268 @default.
• W2891030779 creator A5049074026 @default.
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• W2891030779 date "2019-01-01" @default.
• W2891030779 modified "2023-09-23" @default.
• W2891030779 title "Learning of Tree-Structured Gaussian Graphical Models on Distributed Data Under Communication Constraints" @default.
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• W2891030779 doi "https://doi.org/10.1109/tsp.2018.2876325" @default.
• W2891030779 hasPublicationYear "2019" @default.
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