Exact and Approximate Algorithms for Polytree Learning

TL;DR

This paper introduces new algorithms for learning polytrees, a type of Bayesian network, providing exact solutions under certain restrictions and approximation algorithms with provable guarantees.

Contribution

It presents an improved exact algorithm for polytree learning with time complexity $O((2+\epsilon)^n)$ and new approximation algorithms with bounded factors.

Findings

Optimal polytree can be found in $O((2+\epsilon)^n)$ time, improving previous methods.

Polynomial-time algorithms approximate the optimal polytree within a factor of $k$ or 2.

The paper establishes tight lower bounds for complexity and approximation factors.

Abstract

Polytrees are a subclass of Bayesian networks that seek to capture the conditional dependencies between a set of $n$ variables as a directed forest and are motivated by their more efficient inference and improved interpretability. Since the problem of learning the best polytree is NP-hard, we study which restrictions make it more tractable by considering for example in-degree bounds, properties of score functions measuring the quality of a polytree, and approximation algorithms. We devise an algorithm that finds the optimal polytree in time $O((2+\epsilon)^n)$ for arbitrarily small $\epsilon > 0$ and any constant in-degree bound $k$, improving over the fastest previously known algorithm of time complexity $O(3^n)$. We further give polynomial-time algorithms for finding a polytree whose score is within a factor of $k$ from the optimal one for arbitrary scores and a factor of $2$ for additive ones. Many of the results are complemented by (nearly) tight lower bounds for either the time complexity or the approximation factors.