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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
baseline 1polarities
baseline 1representative citing papers
citing papers explorer
-
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.