TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.