Debiasing Synthetic Data Generated by Deep Generative Models
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| Názov: | Debiasing Synthetic Data Generated by Deep Generative Models |
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| Autori: | Decruyenaere, Alexander, Dehaene, Heidelinde, Rabaey, Paloma, Polet, Christiaan, Decruyenaere, Johan, Demeester, Thomas, Vansteelandt, Stijn |
| Informácie o vydavateľovi: | 2024-11-06 2025-01-15 |
| Druh dokumentu: | Electronic Resource |
| Abstrakt: | While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep generative models (DGMs) for synthetic data generation is known to induce considerable bias and imprecision into synthetic data analyses, compromising their inferential utility as opposed to original data analyses. This bias and uncertainty can be substantial enough to impede statistical convergence rates, even in seemingly straightforward analyses like mean calculation. The standard errors of such estimators then exhibit slower shrinkage with sample size than the typical 1 over root-$n$ rate. This complicates fundamental calculations like p-values and confidence intervals, with no straightforward remedy currently available. In response to these challenges, we propose a new strategy that targets synthetic data created by DGMs for specific data analyses. Drawing insights from debiased and targeted machine learning, our approach accounts for biases, enhances convergence rates, and facilitates the calculation of estimators with easily approximated large sample variances. We exemplify our proposal through a simulation study on toy data and two case studies on real-world data, highlighting the importance of tailoring DGMs for targeted data analysis. This debiasing strategy contributes to advancing the reliability and applicability of synthetic data in statistical inference. Comment: Accepted for the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), joint first authors |
| Témy: | Statistics - Machine Learning, Computer Science - Machine Learning, text |
| URL: | |
| Dostupnosť: | Open access content. Open access content |
| Other Numbers: | COO oai:arXiv.org:2411.04216 1504893260 |
| Prispievajúcí zdroj: | CORNELL UNIV From OAIster®, provided by the OCLC Cooperative. |
| Prístupové číslo: | edsoai.on1504893260 |
| Databáza: | OAIster |
Nájsť tento článok vo Web of Science | Abstrakt: | While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep generative models (DGMs) for synthetic data generation is known to induce considerable bias and imprecision into synthetic data analyses, compromising their inferential utility as opposed to original data analyses. This bias and uncertainty can be substantial enough to impede statistical convergence rates, even in seemingly straightforward analyses like mean calculation. The standard errors of such estimators then exhibit slower shrinkage with sample size than the typical 1 over root-$n$ rate. This complicates fundamental calculations like p-values and confidence intervals, with no straightforward remedy currently available. In response to these challenges, we propose a new strategy that targets synthetic data created by DGMs for specific data analyses. Drawing insights from debiased and targeted machine learning, our approach accounts for biases, enhances convergence rates, and facilitates the calculation of estimators with easily approximated large sample variances. We exemplify our proposal through a simulation study on toy data and two case studies on real-world data, highlighting the importance of tailoring DGMs for targeted data analysis. This debiasing strategy contributes to advancing the reliability and applicability of synthetic data in statistical inference.<br />Comment: Accepted for the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), joint first authors |
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