Influence Approximation

Influence Approximation refers to estimating the impact of triplets in the training data on the behavior of machine learning models. This task aims to identify critical data points by quantifying the contribution of each training sample to the model's prediction results, thereby optimizing model performance and improving data quality. In the context of Methodology, Influence Approximation provides a scientific basis for model debugging and data cleaning, contributing to enhancing the robustness and generalization ability of the model.