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Technical Program

Paper Detail

Paper IDD5-S3-T3.2
Paper Title Fisher Information and Mutual Information Constraints
Authors Leighton Pate Barnes, Ayfer Ozgur, Stanford University, United States
Session D5-S3-T3: Statistics
Chaired Session: Friday, 16 July, 22:40 - 23:00
Engagement Session: Friday, 16 July, 23:00 - 23:20
Abstract We consider the processing of statistical samples $X\sim P_\theta$ by a channel p(y|x), and characterize how the statistical information from the samples for estimating the parameter $\theta\in\mathbb{R}^d$ can scale with the mutual information or capacity of the channel. We show that if the statistical model has a sub-Gaussian score function, then the trace of the Fisher information matrix for estimating $\theta$ from Y can scale at most linearly with the mutual information between X and Y. We apply this result to obtain minimax lower bounds in distributed statistical estimation problems, and obtain a tight preconstant for Gaussian mean estimation. We then show how our Fisher information bound can also imply mutual information or Jensen-Shannon divergence based distributed strong data processing inequalities.