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Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities

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If you have a question about this talk, please contact Dr Varun Jog .

In this talk, we discuss hypothesis testing under communication constraints, where each sample is quantized before being revealed to a statistician. We show that the sample complexity of simple binary hypothesis testing under communication constraints is at most a logarithmic factor larger than in the unconstrained setting and this bound is tight. We develop a polynomial-time algorithm that achieves the aforementioned sample complexity. Our proofs rely on a new reverse data processing inequality and a reverse Markov inequality, which may be of independent interest.

This talk is part of the Information Theory Seminar series.

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Location: Your choice! See email reminders or "Attending Lectures" for details of how to join in. Enterprise Tuesday 2016-2017 GS3, Donald McIntyre Building, Faculty of Education, 184 Hills Road, Cambridge

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