TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
arXiv preprint arXiv:2306.15636 , year=
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Context-conditioned normalizing flows refine subnational survey distributions under severe data scarcity when conditioning covariates capture local heterogeneity.
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Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys
Context-conditioned normalizing flows refine subnational survey distributions under severe data scarcity when conditioning covariates capture local heterogeneity.