# Understanding Black-box Predictions via Influence Functions

@article{Koh2017UnderstandingBP, title={Understanding Black-box Predictions via Influence Functions}, author={Pang Wei Koh and Percy Liang}, journal={ArXiv}, year={2017}, volume={abs/1703.04730} }

How can we explain the predictions of a black-box model. [... ] Key Result On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks. Expand

## 1,632 Citations

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This work takes a novel look at black box interpretation of test predictions in terms of training examples, making use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples.

### Using Cross-Loss Influence Functions to Explain Deep Network Representations

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This work provides the first theoretical and empirical demonstration that influence functions can be extended to handle mismatched training and testing settings and enables us to compute the influence of unsupervised and self-supervised training examples with respect to a supervised test objective.

### Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions

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This paper demonstrates how to make use of influence functions---a well known robust statistic---in the constraints to correct the model’s behaviour more effectively and showcases the effectiveness of RBR in correcting "Clever Hans"-like behaviour in real, high-dimensional domain.

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This paper proposes a novel approach which can be effectively exploited, either in isolation or in combination with other methods, to enhance the interpretability of neural model predictions and shows that its tractability result extends seamlessly to more advanced neural architectures such as convolutional and graph neural networks.

### Explaining Neural Matrix Factorization with Gradient Rollback

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It is shown 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, establishing that gradient roll back is robustly estimating example influence.

### Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

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- 2020

It is found that influence functions are particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation, and a new quantitative measure based on influence functions that can reveal artifacts in training data is developed.

### Infuence functions in Machine Learning tasks

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This work extends the influence functions framework to cover more Machine Learning tasks, so that they can be used more widely in this field to understand and improve training and performance.

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