# Learning Kolmogorov Models for Binary Random Variables

arXiv: https://arxiv.org/abs/1806.02322 · HTML: https://mlsanity.com/paper/1806.02322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent](https://mlsanity.com/paper/2107.05011/) (2021)

## Abstract (arXiv)

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.