39 citations · 48 across the 16 of their papers we have counts for
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cs.LG2023★ 1 cited
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Lennart Purucker, Lennart Schneider, Marie Anastacio +3
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe…
cs.LG2023
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Lennart Purucker, Joeran Beel
Many state-of-the-art automated machine learning (AutoML) systems use greedy ensemble selection (GES) by Caruana et al. (2004) to ensemble models found during model selection post…
cs.LG2023★ 1 cited
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Lennart Purucker, Joeran Beel
Automated Machine Learning (AutoML) frameworks regularly use ensembles. Developers need to compare different ensemble techniques to select appropriate techniques for an AutoML fram…