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