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We consider distributed statistical optimization in one-shot setting, where there are $m$ machines each observing $n$ i.i.d.
Randomized algorithms
Rajeev Motwani and Prabhakar Raghavan · 1995
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Steven G Krantz · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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One-shot distributed learning: theoretical limits and algorithms to achieve them
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