Distributed Statistical Inference under Local Information Constraints
Abstract: We consider statistical inference tasks in a distributed setting where access to data samples is subjected to strict "local constraints," through a unified framework that captures communication limitations and (local) privacy constraints as special cases. We study estimation (learning) and goodness-of-fit (testing) for both discrete and high-dimensional distributions. Our goal is to understand how the sample complexity increases under the information constraints.
In this talk we will provide an overview of this field and a sample of some of our results. We will discuss the role of (public) randomness and interactivity in information-constrained inference, and make a case for thinking about randomness and interactivity as resources.
The work is part of a long-term ongoing collaboration with Clément Canonne (IBM Research) and Himanshu Tyagi (IISc), and includes works done with Cody Freitag (Cornell), Yanjun Han (Stanford), Yuhan Liu (Cornell), and Ziteng Sun (Cornell).
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Contact: Bonnie Leung firstname.lastname@example.org