Data-centric Prediction Explanation via Kernelized Stein Discrepancy
Mahtab Sarvmaili Hassan Sajjad Ga Wu
Existing example-based prediction explanation methods often bridge test and
training data points through the model's parameters or latent representations.
While these methods offer clues to the causes of model predictions, they often
exhibit innate shortcomings, such as incurring significant computational
overhead or producing coarse-grained explanations. This paper presents a
Highly-precise and Data-centric Explanation (HD-Explain), a straightforward
prediction explanation method exploiting properties of Kernelized Stein
Discrepancy (KSD). Specifically, the KSD uniquely defines a parameterized
kernel function for a trained model that encodes model-dependent data
correlation. By leveraging the kernel function, one can identify training
samples that provide the best predictive support to a test point efficiently.
We conducted thorough analyses and experiments across multiple classification
domains, where we show that HD-Explain outperforms existing methods from
various aspects, including 1) preciseness (fine-grained explanation), 2)
consistency, and 3) computation efficiency, leading to a surprisingly simple,
effective, and robust prediction explanation solution.