Greedy PIG: Adaptive Integrated Gradients
Kyriakos Axiotis Sami Abu-al-haija Lin Chen Matthew Fahrbach Gang Fu
Deep learning has become the standard approach for most machine learning
tasks. While its impact is undeniable, interpreting the predictions of deep
learning models from a human perspective remains a challenge. In contrast to
model training, model interpretability is harder to quantify and pose as an
explicit optimization problem. Inspired by the AUC softmax information curve
(AUC SIC) metric for evaluating feature attribution methods, we propose a
unified discrete optimization framework for feature attribution and feature
selection based on subset selection. This leads to a natural adaptive
generalization of the path integrated gradients (PIG) method for feature
attribution, which we call Greedy PIG. We demonstrate the success of Greedy PIG
on a wide variety of tasks, including image feature attribution, graph
compression/explanation, and post-hoc feature selection on tabular data. Our
results show that introducing adaptivity is a powerful and versatile method for
making attribution methods more powerful.