Year: 2021
Type: conference-paper
Abstract: Although Shapley values are theoretically appealing for explaining black-box models, they are costly to calculate and thus impractical in settings that involve large, high-dimensional models. To remedy this issue, we introduce FastSHAP, a new method for estimating Shapley values in a single forward pass using a learned explainer model. To enable efficient training without requiring ground truth Shapley values, we develop an approach to train FastSHAP via stochastic gradient descent using a weighted least squares objective function. In our experiments with tabular and image datasets, we compare FastSHAP to existing estimation approaches and find that it generates accurate explanations with an orders-of-magnitude speedup.
Source: PubMed
Institutions: New York University, University of Washington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CAREER: Learning the Chromatin Network from ChIP-Seq Data, Deep probabilistic predictive models for stroke and coronary heart disease, Collaborative Research: ABI Innovation: Interpretable Machine Learning to Identify Molecular Markers for Complex Phenotypes, NRT-HDR: FUTURE Foundations, Translation, and Responsibility for Data Science Impact, Medical Scientist Research Service Award, G9000094898
OpenAlex ID: W3179224442