Improving Influence Functions for Accurate Modeling of Black-box Predictions by Focusing on Relevant Parameters

핵심 개념

The core message of this paper is that influence functions can be improved by focusing on the parameters that are most relevant to the input data, rather than updating all parameters. This can lead to more accurate and robust model updates when removing or changing training data.

초록

The paper proposes a Generalized Influence Function (GIF) that measures the influence of data on a subset of model parameters, rather than the entire parameter set. This is motivated by the observation that influence functions can become inaccurate as model size and complexity increase, due to the accumulation of errors in irrelevant parameters.

The key aspects of the proposed approach are:

1. Parameter Selection Criteria: The paper introduces four criteria to identify the most relevant parameters for a given input data - Highest-k outputs, Highest-k gradients, Lowest-k outputs, and Lowest-k gradients. These criteria select a subset of parameters to focus the influence computation on.

2. Generalized Influence Function: The GIF formulation computes the influence only on the selected relevant parameters, while nullifying the effects of unselected parameters. This is achieved by projecting the gradient changes onto the subspace of the selected parameters.

3. Modified LiSSA Iteration: The paper proposes a modified version of the LiSSA algorithm, which is used to efficiently compute the GIF. This modified version guarantees convergence without the need for additional regularization.

The experiments show that the GIF outperforms existing influence function methods in data removal and label change tasks, while updating only a small fraction (e.g., 5%) of the model parameters. The updated models closely match the behavior of models retrained from scratch, as demonstrated by output distributions and visualization of discriminative regions.

통계

The test accuracy of the original VGG-11 model on CIFAR-10 dataset is 91.07%.
Updating only 5% of the parameters using the GIF maintains 90.13% test accuracy, while the original influence function (Koh & Liang, 2017) drops to 86.06%.
Updating 5% of the parameters using the GIF achieves 0.26% self-accuracy (on the removed data), compared to 1.29% for the original influence function.

인용구

"Influence functions provide explainability and accessibility to the enigmatic black-box models by measuring how the model changes when some training examples are deleted or perturbed."
"We suspect that the fragility comes from the first-order approximation which may cause nuisance changes in parameters irrelevant to the examined data."

핵심 통찰 요약

Deeper Understanding of Black-box Predictions via Generalized Influence Functions

by Hyeonsu Lyu,... 게시일 arxiv.org 05-07-2024

https://arxiv.org/pdf/2312.05586.pdf

더 깊은 질문

How can the proposed parameter selection criteria be further improved to better identify the most relevant parameters for a given task or input

The proposed parameter selection criteria can be further improved by incorporating more sophisticated techniques to identify the most relevant parameters for a given task or input. One approach could involve leveraging advanced feature selection algorithms, such as recursive feature elimination or L1 regularization, to automatically determine the most influential parameters based on their impact on the model's performance. Additionally, ensemble methods like random forests or gradient boosting could be employed to aggregate the selection criteria from multiple models, providing a more robust and accurate parameter selection process. Furthermore, techniques from network pruning, such as magnitude-based pruning or sensitivity analysis, could be adapted to identify and retain only the most critical parameters for a specific task or input.

What are the potential limitations of the GIF approach in scaling to extremely large models, and how can the computational efficiency be further enhanced

The potential limitations of the GIF approach in scaling to extremely large models primarily revolve around the computational complexity and memory requirements associated with computing influence functions. As models grow in size and complexity, the calculation of the Hessian matrix and its inverse becomes increasingly resource-intensive, leading to scalability issues. To enhance computational efficiency, techniques like low-rank approximation or parallel processing could be employed to reduce the computational burden of computing influence functions for large models. Additionally, leveraging distributed computing frameworks or specialized hardware accelerators, such as GPUs or TPUs, can significantly speed up the computation of influence functions for large-scale models. Furthermore, exploring novel optimization algorithms tailored for large models, such as stochastic optimization or mini-batch processing, can further enhance the computational efficiency of the GIF approach.

How can the insights from the GIF be leveraged to develop more robust and interpretable model update strategies beyond data removal and label change tasks

The insights from the GIF approach can be leveraged to develop more robust and interpretable model update strategies beyond data removal and label change tasks. One potential application is in model debugging and error analysis, where the GIF can be used to identify the parameters responsible for specific model predictions or errors. By analyzing the influence of individual data points on model behavior, practitioners can gain valuable insights into the model's decision-making process and potential areas for improvement. Additionally, the GIF can be utilized in model compression and optimization, where only the most influential parameters are retained, leading to more efficient and streamlined models. Furthermore, the GIF can be integrated into model explainability frameworks to provide users with a clear understanding of how specific inputs impact model predictions, enhancing the overall interpretability of AI systems.