TabHGIF: A Unified Hypergraph Influence Framework for Efficient Unlearning in Tabular Data

TabHGIF is a framework for machine unlearning (removing a model's memory of specific training data) on tabular data, which is faster than retraining from scratch but traditionally struggles to preserve both privacy and model accuracy. The method represents tabular data as a hypergraph (a graph structure that captures relationships between multiple features at once) and uses a Hypergraph Influence Function to predict how deleting data will affect the model without needing to access the original data again. In experiments, TabHGIF achieved speedups of 2.18–7.67 times compared to full retraining while maintaining accuracy close to a fully retrained model.