Revisiting the Fragility of Influence Functions
- URL: http://arxiv.org/abs/2303.12922v2
- Date: Fri, 7 Apr 2023 15:46:06 GMT
- Title: Revisiting the Fragility of Influence Functions
- Authors: Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino, Ghulam Rasool
- Abstract summary: influence functions, which approximates the effect that leave-one-out has on loss, has been proposed to verify accuracy or faithfulness of deep learning models.
Here, we analyze key metrics that are used to validate influence functions.
Our results indicate that the procedures may cause their validation work to be unclear.
- Score: 1.4699455652461724
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the last few years, many works have tried to explain the predictions of
deep learning models. Few methods, however, have been proposed to verify the
accuracy or faithfulness of these explanations. Recently, influence functions,
which is a method that approximates the effect that leave-one-out training has
on the loss function, has been shown to be fragile. The proposed reason for
their fragility remains unclear. Although previous work suggests the use of
regularization to increase robustness, this does not hold in all cases. In this
work, we seek to investigate the experiments performed in the prior work in an
effort to understand the underlying mechanisms of influence function fragility.
First, we verify influence functions using procedures from the literature under
conditions where the convexity assumptions of influence functions are met.
Then, we relax these assumptions and study the effects of non-convexity by
using deeper models and more complex datasets. Here, we analyze the key metrics
and procedures that are used to validate influence functions. Our results
indicate that the validation procedures may cause the observed fragility.
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