用确定性方法提升表格数据填补效率与一致性
MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
- 采用DDIM框架实现确定性采样，避免随机性带来的输出波动
- 推理速度比传统方法快3倍以上，适合实时场景
- 特别适合对结果可复现性要求高的金融、医疗等表格数据应用
扩散模型近年来被用于建模观测与未观测变量的联合分布，以实现缺失数据填补。然而，现有基于随机去噪扩散概率模型（DDPM）的方法存在推理延迟高、输出不一致的问题，限制了其在真实表格数据场景中的应用。为此，本文提出MissDDIM，一种将去噪扩散隐式模型（DDIM）适配于表格填补的条件扩散框架。虽然随机采样能生成多样化补全结果，但也引入输出波动，增加下游处理复杂度。本方法通过确定性采样，在保证填补质量的同时显著降低延迟并提升输出一致性。
原文摘要 · Abstract (English)
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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