Self-Supervision Improves Diffusion Models for Tabular Data Imputation (2407.18013v1)

Abstract: The ubiquity of missing data has sparked considerable attention and focus on tabular data imputation methods. Diffusion models, recognized as the cutting-edge technique for data generation, demonstrate significant potential in tabular data imputation tasks. However, in pursuit of diversity, vanilla diffusion models often exhibit sensitivity to initialized noises, which hinders the models from generating stable and accurate imputation results. Additionally, the sparsity inherent in tabular data poses challenges for diffusion models in accurately modeling the data manifold, impacting the robustness of these models for data imputation. To tackle these challenges, this paper introduces an advanced diffusion model named Self-supervised imputation Diffusion Model (SimpDM for brevity), specifically tailored for tabular data imputation tasks. To mitigate sensitivity to noise, we introduce a self-supervised alignment mechanism that aims to regularize the model, ensuring consistent and stable imputation predictions. Furthermore, we introduce a carefully devised state-dependent data augmentation strategy within SimpDM, enhancing the robustness of the diffusion model when dealing with limited data. Extensive experiments demonstrate that SimpDM matches or outperforms state-of-the-art imputation methods across various scenarios.

• The paper introduces SimpDM, a novel model integrating self-supervision to mitigate noise sensitivity and align imputation predictions with observed data.
• The paper employs state-dependent data augmentation to expand limited tabular datasets, improving performance across diverse missing data scenarios.
• Empirical evaluations on 17 datasets show that SimpDM outperforms both traditional and deep learning imputation methods under high missing ratios.

Self-Supervision Enhances Diffusion Models in Tabular Data Imputation

The paper "Self-Supervision Improves Diffusion Models for Tabular Data Imputation" addresses a persistent challenge in machine learning: the imputation of missing values within tabular datasets. Tabular data imputation is critical given the prevalence of missing entries in datasets due to factors such as human error or privacy considerations. This paper offers a novel approach by introducing the Self-supervised Imputation Diffusion Model (SimpDM), tailored to improve the efficiency of diffusion models in this context.

Core Contributions

The authors identify two primary deficiencies in conventional diffusion models when applied to tabular data imputation: objective mismatch and data scale mismatch. These mismatches arise from diffusion models' inherent sensitivity to initialized noise and their requirement of large datasets to effectively learn the data manifold. SimpDM introduces key innovations to overcome these challenges.

1. Self-Supervised Alignment: This mechanism regularizes the model's output by ensuring consistent imputation predictions, leveraging similarities in observed data despite varying initial noise. This technique mitigates the sensitivity to initialized noise typical of diffusion models, aligning outcomes more closely with known data characteristics.
2. State-Dependent Data Augmentation: To address the data scale mismatch, SimpDM employs augmentation strategies that enhance the robustness of the imputation model. This involves perturbation-based data augmentation specifically designed for tabular data, expanding the dataset and improving the model's ability to generalize from limited samples.

Methodological Approach

The SimpDM methodology builds on existing diffusion models but specifically adapts them for imputation tasks. The combination of Gaussian and multinomial diffusion processes accommodates the mixed-type nature of tabular data, optimizing the imputation of both numerical and categorical attributes.

Training leverages a pseudo-mask strategy, which allows for the model to be trained even when the ground truth for missing values is unknown. By creating pseudo-missing values from observed data, the model can be supervised in its predictions.

Empirical Evaluation and Findings

The paper presents an extensive empirical evaluation across 17 diverse datasets. SimpDM consistently matches or surpasses state-of-the-art imputation methods, including both traditional statistical approaches and modern machine learning techniques such as GAN-based and VAE-based imputation models. Its robust performance is particularly notable under conditions of high missing data ratios and in various missing data scenarios (MAR and MNAR).

The model's architecture—primarily a shallow MLP—reflects a strategic simplification aimed at reducing computational overhead and mitigating overfitting risks inherent in complex model architectures faced with smaller tabular datasets.

Implications and Future Directions

SimpDM’s successful application showcases the potential of integrating self-supervision into diffusion models to enhance their adaptability and robustness in data imputation tasks. The introduction of state-dependent augmentation represents a significant stride toward making generative models viable for real-world tabular data applications.

Future research could explore the extension of these techniques beyond imputation to other tabular learning tasks, such as classification or regression. Additionally, refining the self-supervised loss functions and augmentation strategies could yield further improvements, potentially applying these insights to more complex domains such as time-series or multimodal data.

In summary, this paper presents a technically rigorous approach to improve tabular data imputation through diffusion models enhanced by self-supervision and intelligent data augmentation, providing both theoretical insights and practical tools for the broader machine learning community.