Learning Kolmogorov Models for Binary Random Variables

TL;DR

This paper introduces a framework for learning Kolmogorov models for binary variables, providing conditions for variable relations, an algorithm with proven optimality, and applications in recommendation systems, advancing interpretable machine learning.

Contribution

It presents a novel approach for learning Kolmogorov models with an efficient algorithm and theoretical guarantees, applicable to recommendation systems and beyond.

Findings

Derived conditions linking binary variable outcomes

Proposed an algorithm with first-order optimality

Applied the model to recommendation systems

Abstract

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.