title: Evaluations and Methods for Explanation through Robustness Analysis
abstract: 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 way to explain a model. In this paper, we establish the link between a set of features to a prediction with a new evaluation criteria, robustness analysis, which measures the minimum tolerance of adversarial perturbation. By measuring the tolerance level for an adversarial attack, we can extract a set of features that provides most robust support for a current prediction, and also can extract a set of features that contrasts the current prediction to a target class by setting a targeted adversarial attack. By applying this methodology to various prediction tasks across multiple domains, we observed the derived explanations are indeed capturing the significant feature set qualitatively and quantitatively.
authors: Cheng-Yu Hsieh Chih-Kuan Yeh Xuanqing Liu Pradeep Ravikumar Seungyeon Kim Sanjiv Kumar Cho-Jui Hsieh
hIndex: Cho-Jui Hsieh 58