  We summarize our recent findings, where we proposed a framework for learning
a Kolmogorov model, for a collection of binary random variables. More
specifically, we derive conditions that link outcomes of specific random
variables, and extract valuable relations from the data. We also propose an
algorithm for computing the model and show its first-order optimality, despite
the combinatorial nature of the learning problem. We apply the proposed
algorithm to recommendation systems, although it is applicable to other
scenarios. We believe that the work is a significant step toward interpretable
machine learning.
