Unsupervised Ensemble Learning Through Deep Energy-based Models

TL;DR

This paper introduces a deep energy-based model for unsupervised ensemble learning that effectively combines multiple predictors without labeled data, capturing complex dependencies and demonstrating superior performance across various scenarios.

Contribution

The paper presents a novel deep energy-based approach for unsupervised ensemble learning that requires no labeled data and can model complex dependencies among learners.

Findings

Outperforms existing methods on diverse ensemble datasets.

Capable of modeling complex dependence structures.

Theoretically guarantees under conditional independence assumptions.

Abstract

Unsupervised ensemble learning emerged to address the challenge of combining multiple learners' predictions without access to ground truth labels or additional data. This paradigm is crucial in scenarios where evaluating individual classifier performance or understanding their strengths is challenging due to limited information. We propose a novel deep energy-based method for constructing an accurate meta-learner using only the predictions of individual learners, potentially capable of capturing complex dependence structures between them. Our approach requires no labeled data, learner features, or problem-specific information, and has theoretical guarantees for when learners are conditionally independent. We demonstrate superior performance across diverse ensemble scenarios, including challenging mixture of experts settings. Our experiments span standard ensemble datasets and curated datasets designed to test how the model fuses expertise from multiple sources. These results highlight the potential of unsupervised ensemble learning to harness collective intelligence, especially in data-scarce or privacy-sensitive environments.