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• W2946277198 abstract "A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We study problems of distribution learning and identity testing in this distributed inference setting and examine the role of shared randomness as a resource. We propose a general-purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning. This general strategy turns out to be sample-optimal even for distribution testing among private-coin protocols. Interestingly, we propose a public-coin protocol that outperforms simulate-and-infer for distribution testing and is, in fact, sample-optimal. Underlying our public-coin protocol is a random hash that when applied to the samples minimally contracts the chi-squared distance of their distribution to the uniform distribution." @default.
• W2946277198 created "2019-05-29" @default.
• W2946277198 creator A5022867638 @default.
• W2946277198 creator A5071869723 @default.
• W2946277198 creator A5077741905 @default.
• W2946277198 date "2019-05-20" @default.
• W2946277198 modified "2023-09-25" @default.
• W2946277198 title "Inference under Information Constraints II: Communication Constraints and Shared Randomness" @default.
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• W2946277198 hasPublicationYear "2019" @default.
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