Web: http://arxiv.org/abs/2206.11126

June 23, 2022, 1:11 a.m. | Liu Zhendong, Wenyu Jiang, Yi Zhang, Chongjun Wang

cs.LG updates on arXiv.org arxiv.org

With the rapid development of eXplainable Artificial Intelligence (XAI), a
long line of past work has shown concerns about the Out-of-Distribution (OOD)
problem in perturbation-based post-hoc XAI models and explanations are socially
misaligned. We explore the limitations of post-hoc explanation methods that use
approximators to mimic the behavior of black-box models. Then we propose
eXplanation-based Counterfactual Retraining (XCR), which extracts feature
importance fastly. XCR applies the explanations generated by the XAI model as
counterfactual input to retrain the black-box model …

arxiv lg models

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY