Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918194918195200 |
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| author | Li, Zuqing Gan, Junhao Qi, Jianzhong |
| author_facet | Li, Zuqing Gan, Junhao Qi, Jianzhong |
| contents | Diffusion-based tabular data synthesis models have yielded promising results. However, when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dimensional space often hinder generative models from capturing the distribution accurately. To mitigate the insufficient learning signals and to stabilize training under such conditions, we propose CtrTab, a condition-controlled diffusion model that injects perturbed ground-truth samples as auxiliary inputs during training. This design introduces an implicit L2 regularization on the model's sensitivity to the control signal, improving robustness and stability in high-dimensional, low-data scenarios. Experimental results across multiple datasets show that CtrTab outperforms state-of-the-art models, with a performance gap in accuracy over 90% on average. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_06444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Synthesizing High-Dimensional Tabular Data with Limited Samples Li, Zuqing Gan, Junhao Qi, Jianzhong Machine Learning Artificial Intelligence Databases Diffusion-based tabular data synthesis models have yielded promising results. However, when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dimensional space often hinder generative models from capturing the distribution accurately. To mitigate the insufficient learning signals and to stabilize training under such conditions, we propose CtrTab, a condition-controlled diffusion model that injects perturbed ground-truth samples as auxiliary inputs during training. This design introduces an implicit L2 regularization on the model's sensitivity to the control signal, improving robustness and stability in high-dimensional, low-data scenarios. Experimental results across multiple datasets show that CtrTab outperforms state-of-the-art models, with a performance gap in accuracy over 90% on average. |
| title | Towards Synthesizing High-Dimensional Tabular Data with Limited Samples |
| topic | Machine Learning Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2503.06444 |