MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation

• URL: http://arxiv.org/abs/2508.03083v1
• Date: Tue, 05 Aug 2025 04:55:26 GMT
• Title: MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
• Authors: Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal,
• Abstract summary: We present MissDDIM, a conditional diffusion framework that adapts Denoising Diffusion Implicit Models (DDIM) for tabular imputation.<n>While sampling enables diverse completions, it also introduces output variability that complicates downstream processing.
• Score: 2.124791625488617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have recently emerged as powerful tools for missing data imputation by modeling the joint distribution of observed and unobserved variables. However, existing methods, typically based on stochastic denoising diffusion probabilistic models (DDPMs), suffer from high inference latency and variable outputs, limiting their applicability in real-world tabular settings. To address these deficiencies, we present in this paper MissDDIM, a conditional diffusion framework that adapts Denoising Diffusion Implicit Models (DDIM) for tabular imputation. While stochastic sampling enables diverse completions, it also introduces output variability that complicates downstream processing.

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