AI RESEARCH
LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution
arXiv CS.LG
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Training data attribution (TDA) identifies which training examples most influenced a model's prediction. Influence function methods are a theoretically grounded family of TDA methods and exploit gradients. To overcome the scalability challenge arising from gradient computation, the most popular strategy is random projection (e.g., TRAK, LoGRA).