A tabular data generation framework guided by downstream tasks optimization.

ABSTRACT: Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which cannot generate high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. Responding to the challenge, we propose a tabular data generation framework guided by downstream task optimization (TDGGD). It incorporates three indicators into each time step of diffusion generation, using gradient optimization to align the generated fake data. Unlike the traditional strategy of separating the downstream task model from the upstream data synthesis model, TDGGD ensures that the generated dat

SUBMITTER: Jia F 

PROVIDER: S-EPMC11757725 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature