Differentiably Discovering Sets of Rules

Association rule mining is a great way to gain insight into complex data, such as single-cell gene expression data, as it allows discovering conditional dependencies of the kind 'In samples where high expression of gene A and B are observed, C and D tend to be expressed too '. Or at least, in theory. In practice, traditional methods for mining association rules tend to overwhelm the user with overly many, highly redundant, and mostly spurious results, while modern approaches that fix those problems do not scale to high-dimensional data. In this paper, we propose an end-to-end differentiable approach for mining high quality sets of rules that does scale to hundreds of thousands of features.