Adversarial Infidelity Learning for Model Interpretation

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020, pp. 286-296, 2020.

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We propose an adversarial infidelity learning mechanism to screen relative unimportant features for mitigating the combinatorial shortcuts and the model identifiability problems

Abstract:

Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characteristics. A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature represent...More

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