Specialization in Hierarchical Learning Systems

TL;DR

This paper introduces an information-theoretic online learning method that enables hierarchical systems to automatically partition problems and develop specialized experts, improving decision-making across various machine learning tasks.

Contribution

It proposes a novel regularization approach based on information constraints that promotes specialization and problem partitioning in hierarchical learning systems.

Findings

Effective in classification, regression, density estimation, and reinforcement learning.

Enables meta-learning by specializing experts on task sets.

Demonstrates broad applicability across machine learning domains.

Abstract

Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization. We investigate in how far information constraints in hierarchies of experts not only provide a principled method for regularization but also to enforce specialization. In particular, we devise an information-theoretically motivated on-line learning rule that allows partitioning of the problem space into multiple sub-problems that can be solved by the individual experts. We demonstrate two different ways to apply our method: (i) partitioning problems based on individual data samples and (ii) based on sets of data samples representing tasks. Approach (i) equips the system with the ability to solve complex decision-making problems by finding an optimal combination of local expert decision-makers. Approach (ii) leads to decision-makers specialized in solving families of tasks, which equips the system with the ability to solve meta-learning problems. We show the broad applicability of our approach on a range of problems including classification, regression, density estimation, and reinforcement learning problems, both in the standard machine learning setup and in a meta-learning setting.