MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
This work addresses a practical issue for users of tabular data imputation, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of high inference latency and variable outputs in diffusion models for tabular data imputation by introducing MissDDIM, a conditional framework based on DDIM, which achieved deterministic and efficient imputation.
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.