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Home
Understanding Data Influence With Differential Approximation.

Home
Understanding Data Influence With Differential Approximation.

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Understanding Data Influence With Differential Approximation.

Haoru Tan, Sitong Wu, Xiuzhe Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 4, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces Diff-In, a novel method for approximating data influence in artificial intelligence model training. Diff-In offers accurate, scalable, and computationally efficient data analysis, improving model performance and data utilization.

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Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Accurate quantitative analysis of data is crucial for efficient and high-quality artificial intelligence model training.
Existing data analysis tools often lack accuracy and make simplifying assumptions, such as model convexity, hindering effective implementation.
Limitations in current methods necessitate the development of more robust and accurate data influence approximation techniques.

Purpose of the Study:

To introduce a new formulation, Diff-In, for approximating sample influence in machine learning models.
To develop a method that accurately estimates data influence without requiring model convexity assumptions.
To provide a computationally efficient and scalable solution for data-centric tasks in artificial intelligence.

Main Methods:

Formulated sample-wise influence as the cumulative sum of differences across successive training iterations.
Employed second-order approximations to accurately estimate difference terms, bypassing the need for model convexity.
Achieved computational complexity comparable to first-order methods by efficiently approximating Hessian-gradient products using finite differences.

Main Results:

Theoretical analysis demonstrated significantly lower approximation error for Diff-In compared to existing influence estimators.
Empirical evaluations confirmed superior performance across multiple benchmark datasets in data cleaning, deletion, and coreset selection.
Experiments on large-scale vision-language pre-training showed Diff-In scales to millions of data points and outperforms baseline methods.

Conclusions:

Diff-In provides a theoretically sound and empirically validated approach for accurate sample influence approximation.
The method offers a scalable and computationally efficient alternative to existing techniques for data-centric AI tasks.
Diff-In enhances data utilization and model performance, particularly in large-scale machine learning applications.