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Found 3 papers, 2 papers with code
Discovering Reliable Correlations in Categorical Data
This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data.
Discovering Reliable Approximate Functional Dependencies
As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed.
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