mlsanityData to 7 Oct 2026

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML

arXiv 2307.08364 ↗First posted 17 Jul 2023cs.LG cs.NEcited by 8

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Abstract (arXiv)

Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe that GES may not always be optimal, as it performs a simple deterministic greedy search. In this work, we introduce two novel population-based ensemble selection methods, QO-ES and QDO-ES, and compare them to GES. While QO-ES optimises solely for predictive performance, QDO-ES also considers the diversity of ensembles within the population, maintaining a diverse set of well-performing ensembles during optimisation based on ideas of quality diversity optimisation. The methods are evaluated using 71 classification datasets from the AutoML benchmark, demonstrating that QO-ES and QDO-ES often outrank GES, albeit only statistically significant on validation data. Our results further suggest that diversity can be beneficial for post hoc ensembling but also increases the risk of overfitting.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 18 of its 82 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

18 of 82 references.

References · 18

  1. 2014OpenML: networked science in machine learningcited by 523
  2. 2015Illuminating search spaces by mapping elitescited by 406
  3. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
  4. 2014Robots that can adapt like animalscited by 203
  5. 2020Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningcited by 174
  6. 2019FLAML: A Fast and Lightweight AutoML Librarycited by 104
  7. 2016Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Sciencecited by 73
  8. 2023A Unified Theory of Diversity in Ensemble Learningcited by 71
  9. 2020Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLcited by 67
  10. 2020Quality-Diversity Optimization: a novel branch of stochastic optimizationcited by 59
  11. 2022AMLB: an AutoML Benchmarkcited by 55
  12. 2019Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundariescited by 32
6 more
  1. 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
  2. 2023pyribs: A Bare-Bones Python Library for Quality Diversity Optimizationcited by 29
  3. 2019The advantages of multiple classes for reducing overfitting from test set reusecited by 14
  4. 2018The Algorithm Selection Competitions 2015 and 2017cited by 11
  5. 2023Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenMLcited by 8
  6. 2022Quality Diversity Evolutionary Learning of Decision Treescited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 4.

8 papers.

  1. 2025PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuningcited by 4
  2. 2024Regularized Neural Ensemblerscited by 1
  3. 2024Ensembling Finetuned Language Models for Text Classificationcited by 1
  4. 2026HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Datacited by 1
  5. 2024Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Costcited by 0
  6. 2026Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systemscited by 0
  7. 2026TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Versioncited by 0
  8. 2026SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensemblingcited by 0