Influence Approximation
4 papers with code • 0 benchmarks • 0 datasets
Estimating the influence of training triples on the behavior of a machine learning model.
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Most implemented papers
On the Accuracy of Influence Functions for Measuring Group Effects
kohpangwei/group-influence-release
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NeurIPS 2019
Influence functions estimate the effect of removing a training point on a model without the need to retrain.
Explaining Neural Matrix Factorization with Gradient Rollback
carolinlawrence/gradient-rollback
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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 descent.
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
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.
Deeper Understanding of Black-box Predictions via Generalized Influence Functions
hslyu/gif
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However, growing non-convexity and the number of parameters in modern large-scale models lead to imprecise influence approximation and instability in computations.
