Influence Approximation

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Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• On the Accuracy of Influence Functions for Measuring Group Effects
  30 May 2019 2 repositories listed
  Influence functions estimate the effect of removing a training point on a model without the need to retrain.
• Deeper Understanding of Black-box Predictions via Generalized Influence Functions
  9 Dec 2023 1 repository listed
  Influence functions (IFs) elucidate how training data changes model behavior.
• DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
  2 Oct 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline.
• Explaining Neural Matrix Factorization with Gradient Rollback
  12 Oct 2020 1 repository listed
  Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.