MetaEns trains on meta-datasets to predict marginal gains from adding models and uses a submodular-inspired objective with diversity discounting and risk regularization for greedy unsupervised ensemble selection, outperforming baselines on 39 datasets.
Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) , pages =
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Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version
MetaEns trains on meta-datasets to predict marginal gains from adding models and uses a submodular-inspired objective with diversity discounting and risk regularization for greedy unsupervised ensemble selection, outperforming baselines on 39 datasets.