Toward Efficient Influence Function: Dropout as a Compression Tool

arXiv:2509.15651v2 Announce Type: replace-cross Abstract: Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency, and selecting training data. Influence function provides a theoretical framework for quantifying the effect of training data points on model's performance given a specific test data. However, the computational and memory costs of influence function presents significant challenges, especially