MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation

TL;DR

MissDDIM introduces a deterministic, efficient diffusion-based method for tabular data imputation, reducing inference latency and output variability compared to traditional stochastic models.

Contribution

This paper adapts DDIM for tabular data imputation, providing a deterministic framework that improves efficiency and consistency over existing stochastic diffusion models.

Findings

Reduces inference latency in tabular data imputation.

Provides more consistent and deterministic imputation outputs.

Achieves competitive or superior imputation accuracy.

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.