Meta-learning approach for variational autoencoder hyperparameter tuning
Synthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-lear...
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2025-06-01
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| Series: | Journal of Universal Computer Science |
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| Online Access: | https://lib.jucs.org/article/124087/download/pdf/ |
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| _version_ | 1849431591507460096 |
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| author | Michele Berti Matheus Camilo da Silva Sebastiano Saccani Sylvio Barbon Junior |
| author_facet | Michele Berti Matheus Camilo da Silva Sebastiano Saccani Sylvio Barbon Junior |
| author_sort | Michele Berti |
| collection | DOAJ |
| description | Synthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-learning (MtL) method for hyperparameter recommendation, which achieves competitive performance to state-of-the-art Bayesian Optimization (BO) with median AUC values of 0.660 ± 0.038 (MtL) and 0.650 ± 0.041 (BO), showing no statistically significant difference. Notably, our approach reduces configuration time to under three minutes, compared to BO’s multi-hour requirement, while also enabling incremental improvements through new data integration. This combination of efficiency, adaptability, and performance establishes MtL as a practical solution for hyperparameter tuning in synthetic data generation. |
| format | Article |
| id | doaj-art-f646b0c98dba4398b1d9d571fb1acc61 |
| institution | Kabale University |
| issn | 0948-6968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj-art-f646b0c98dba4398b1d9d571fb1acc612025-08-20T03:27:36ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682025-06-0131766868210.3897/jucs.124087124087Meta-learning approach for variational autoencoder hyperparameter tuningMichele Berti0Matheus Camilo da Silva1Sebastiano Saccani2Sylvio Barbon Junior3University of TriesteUniversity of TriesteAindo SrlUniversity of TriesteSynthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-learning (MtL) method for hyperparameter recommendation, which achieves competitive performance to state-of-the-art Bayesian Optimization (BO) with median AUC values of 0.660 ± 0.038 (MtL) and 0.650 ± 0.041 (BO), showing no statistically significant difference. Notably, our approach reduces configuration time to under three minutes, compared to BO’s multi-hour requirement, while also enabling incremental improvements through new data integration. This combination of efficiency, adaptability, and performance establishes MtL as a practical solution for hyperparameter tuning in synthetic data generation.https://lib.jucs.org/article/124087/download/pdf/HPOMeta-learningVariational AutoencodersVAE |
| spellingShingle | Michele Berti Matheus Camilo da Silva Sebastiano Saccani Sylvio Barbon Junior Meta-learning approach for variational autoencoder hyperparameter tuning Journal of Universal Computer Science HPO Meta-learning Variational Autoencoders VAE |
| title | Meta-learning approach for variational autoencoder hyperparameter tuning |
| title_full | Meta-learning approach for variational autoencoder hyperparameter tuning |
| title_fullStr | Meta-learning approach for variational autoencoder hyperparameter tuning |
| title_full_unstemmed | Meta-learning approach for variational autoencoder hyperparameter tuning |
| title_short | Meta-learning approach for variational autoencoder hyperparameter tuning |
| title_sort | meta learning approach for variational autoencoder hyperparameter tuning |
| topic | HPO Meta-learning Variational Autoencoders VAE |
| url | https://lib.jucs.org/article/124087/download/pdf/ |
| work_keys_str_mv | AT micheleberti metalearningapproachforvariationalautoencoderhyperparametertuning AT matheuscamilodasilva metalearningapproachforvariationalautoencoderhyperparametertuning AT sebastianosaccani metalearningapproachforvariationalautoencoderhyperparametertuning AT sylviobarbonjunior metalearningapproachforvariationalautoencoderhyperparametertuning |