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Modeling Tabular data using Conditional GAN
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train TGAN instead of using the original data as the synthetic-data reference | TGAN · adult | F1: 0.669 -> 0.601 | Hurt |
| Train TGAN instead of using the original data as the synthetic-data reference | TGAN · credit | F1: 0.720 -> 0.672 | No clear effect |
| Train TGAN instead of using the original data as the synthetic-data reference | TGAN · asia | L_test: -2.24 -> -2.31 | Hurt |
| Train TGAN instead of using the original data as the synthetic-data reference | TGAN · alarm | L_test: -10.3 -> -12.6 | Hurt |
Closest papers by meaning
- CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing (AAAI 2025 · similarity 0.92)
- TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data (ICML 2025 · similarity 0.85)
- CTSyn: A Foundation Model for Cross Tabular Data Generation (ICLR 2025 · similarity 0.85)
- How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data (ICLR 2024 · similarity 0.85)
- TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data (ICML 2025 · similarity 0.84)
- Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer (ICML 2025 · similarity 0.84)
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