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Over-the-Air Statistical Estimation
We study schemes and lower bounds for distributed minimax statistical estimation over a Gaussian multiple-access channel (MAC) under squared error loss, in a framework combining statistical estimation and wireless communication.
Fisher Information and Mutual Information Constraints
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.
Information Theory Information Theory Statistics Theory Statistics Theory
rTop-k: A Statistical Estimation Approach to Distributed SGD
The statistically optimal communication scheme arising from the analysis of this model leads to a new sparsification technique for SGD, which concatenates random-k and top-k, considered separately in the prior literature.
Fisher information under local differential privacy
We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon$ under local differential privacy constraints.
Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information
We consider the problem of learning high-dimensional, nonparametric and structured (e. g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use $k$ bits to communicate its sample to a central processor.
