Parameter Averaging for Robust Explainability
- URL: http://arxiv.org/abs/2208.03249v1
- Date: Fri, 5 Aug 2022 16:00:07 GMT
- Title: Parameter Averaging for Robust Explainability
- Authors: Talip Ucar, Ehsan Hajiramezanali
- Abstract summary: We introduce a novel method based on parameter averaging for robust explainability in tabular data setting, referred as XTab.
We conduct extensive experiments on a variety of real and synthetic datasets, demonstrating that the proposed method can be used for feature selection as well as to obtain the global feature importance that are not sensitive to sub-optimal model initialisation.
- Score: 5.032906900691182
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neural Networks are known to be sensitive to initialisation. The explanation
methods that rely on neural networks are not robust since they can have
variations in their explanations when the model is initialized and trained with
different random seeds. The sensitivity to model initialisation is not
desirable in many safety critical applications such as disease diagnosis in
healthcare, in which the explainability might have a significant impact in
helping decision making. In this work, we introduce a novel method based on
parameter averaging for robust explainability in tabular data setting, referred
as XTab. We first initialize and train multiple instances of a shallow network
(referred as local masks) with different random seeds for a downstream task. We
then obtain a global mask model by "averaging the parameters" of local masks
and show that the global model uses the majority rule to rank features based on
their relative importance across all local models. We conduct extensive
experiments on a variety of real and synthetic datasets, demonstrating that the
proposed method can be used for feature selection as well as to obtain the
global feature importance that are not sensitive to sub-optimal model
initialisation.
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