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

Add to your list(s) Download to your calendar using vCal

• Dr Varun Jog, University of Cambridge
• Wednesday 19 October 2022, 14:00-15:00
• MR5, CMS Pavilion A.

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.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Hanchen DaDaDash
• Information Theory Seminar
• Interested Talks
• MR5, CMS Pavilion A
• School of Physical Sciences
• Statistical Laboratory info aggregator
• bld31

Note that ex-directory lists are not shown.