Unsupervised Ensemble Learning Through Deep Energy-based Models

Ariel Maymon, Yanir Buznah, Uri Shaham
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:91-99, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-maymon26a, title = { Unsupervised Ensemble Learning Through Deep Energy-based Models }, author = {Maymon, Ariel and Buznah, Yanir and Shaham, Uri}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {91--99}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/maymon26a/maymon26a.pdf}, url = {https://proceedings.mlr.press/v300/maymon26a.html}, 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. } }
Endnote
%0 Conference Paper %T Unsupervised Ensemble Learning Through Deep Energy-based Models %A Ariel Maymon %A Yanir Buznah %A Uri Shaham %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-maymon26a %I PMLR %P 91--99 %U https://proceedings.mlr.press/v300/maymon26a.html %V 300 %X 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.
APA
Maymon, A., Buznah, Y. & Shaham, U.. (2026). Unsupervised Ensemble Learning Through Deep Energy-based Models . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:91-99 Available from https://proceedings.mlr.press/v300/maymon26a.html.

Related Material