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DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
March 22, 2024, 4:41 a.m. | Yizhu Wen, Kai Yi, Jing Ke, Yiqing Shen
cs.LG updates on arXiv.org arxiv.org
Abstract: Tabular data plays a crucial role in various domains but often suffers from missing values, thereby curtailing its potential utility. Traditional imputation techniques frequently yield suboptimal results and impose substantial computational burdens, leading to inaccuracies in subsequent modeling tasks. To address these challenges, we propose DiffImpute, a novel Denoising Diffusion Probabilistic Model (DDPM). Specifically, DiffImpute is trained on complete tabular datasets, ensuring that it can produce credible imputations for missing entries without undermining the authenticity …
arxiv cs.ai cs.db cs.lg data denoising diffusion imputation probabilistic model tabular tabular data type
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