arXiv AI
Causal-Aware Tabular GANs with Reinforcement Learning
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Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
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Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
arXiv:2603. 10254v2 Announce Type: replace Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains.
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A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces
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Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
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Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
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EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation
EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.