arXiv Machine Learning August 3, 2026 By Nitish Nagesh, Mahdi Bagheri, Amir M. Rahmani

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

Read the original on arXiv Machine Learning →

arXiv:2607. 28945v1 Announce Type: new Abstract: Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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