# Unsupervised Ensemble Learning with Dependent Classifiers

arXiv: https://arxiv.org/abs/1510.05830 · HTML: https://mlsanity.com/paper/1510.05830/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2015.

## Built on by (most cited first, 19 of 19)

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- [Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning](https://mlsanity.com/paper/2109.01381/) (2021)
- [Copula Quadrant Similarity for Anomaly Scores](https://mlsanity.com/paper/2101.02330/) (2021)
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- [Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes](https://mlsanity.com/paper/2202.03287/) (2022)
- [Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification](https://mlsanity.com/paper/2211.09940/) (2022)
- [Aggregating Correlated Estimations with (Almost) no Training](https://mlsanity.com/paper/2309.02005/) (2023)
- [Ranking and Combining Latent Structured Predictive Scores without Labeled Data](https://mlsanity.com/paper/2408.07796/) (2024)
- [A logical alarm for misaligned binary classifiers](https://mlsanity.com/paper/2409.11052/) (2024)
- [Logical Consistency Between Disagreeing Experts and Its Role in AI Safety](https://mlsanity.com/paper/2510.00821/) (2025)
- [Unsupervised Ensemble Learning Through Deep Energy-based Models](https://mlsanity.com/paper/2601.20556/) (2026)
- [SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction](https://mlsanity.com/paper/2603.10215/) (2026)
- [RAIM: Robust Aggregation of Inexpensive Models for Hallucination Detection](https://mlsanity.com/paper/2609.39229/) (2026)

## Abstract (arXiv)

In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it. The task is to combine these possibly conflicting predictions into an accurate meta-learner. Most works to date assumed perfect diversity between the different sources, a property known as conditional independence. In realistic scenarios, however, this assumption is often violated, and ensemble learners based on it can be severely sub-optimal. The key challenges we address in this paper are: (i) how to detect, in an unsupervised manner, strong violations of conditional independence; and (ii) construct a suitable meta-learner. To this end we introduce a statistical model that allows for dependencies between classifiers. Our main contributions are the development of novel unsupervised methods to detect strongly dependent classifiers, better estimate their accuracies, and construct an improved meta-learner. Using both artificial and real datasets, we showcase the importance of taking classifier dependencies into account and the competitive performance of our approach.