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Poster
Discovering Potential Correlations via Hypercontractivity
Hyeji Kim · Weihao Gao · Sreeram Kannan · Sewoong Oh · Pramod Viswanath

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #223
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Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discovering average correlation, they fail to discover hidden or potential correlations. To bridge this gap, (i) we postulate a set of natural axioms that we expect a measure of potential correlation to satisfy; (ii) we show that the rate of information bottleneck, i.e., the hypercontractivity coefficient, satisfies all the proposed axioms; (iii) we provide a novel estimator to estimate the hypercontractivity coefficient from samples; and (iv) we provide numerical experiments demonstrating that this proposed estimator discovers potential correlations among various indicators of WHO datasets, is robust in discovering gene interactions from gene expression time series data, and is statistically more powerful than the estimators for other correlation measures in binary hypothesis testing of canonical examples of potential correlations.

Keywords: Information Theory · Components Analysis (e.g., CCA, ICA, LDA, PCA) · Causal Inference

Author Information

Hyeji Kim (Samsung AI Center Cambridge)
Weihao Gao (UIUC)
Sreeram Kannan (University of Washington)
Sewoong Oh (UIUC)
Pramod Viswanath (UIUC)

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