Evaluations and Methods for Explanation through Robustness Analysis

ICLR 2020 Cheng-Yu HsiehChih-Kuan YehXuanqing LiuPradeep RavikumarSeungyeon KimSanjiv KumarCho-Jui Hsieh

Among multiple ways of interpreting a machine learning model, measuring the importance of a set of features tied to a prediction is probably one of the most intuitive ways to explain a model. In this paper, we establish the link between a set of features to a prediction with a new evaluation criterion, robustness analysis, which measures the minimum distortion distance of adversarial perturbation... (read more)

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