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Exponential Concentration for Mutual Information Estimation with Application to Forests
Han Liu · John Lafferty · Larry Wasserman

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

We prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information. Previous results on mutual information estimation only bounded expected error. The advantage of having the exponential inequality is that, combined with the union bound, we can guarantee accurate estimators of the mutual information for many pairs of random variables simultaneously. As an application, we show how to use such a result to optimally estimate the density function and graph of a distribution which is Markov to a forest graph.

Author Information

Han Liu (Tencent AI Lab)
John Lafferty (Yale University)
Larry Wasserman (Carnegie Mellon University)

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