32 citations · 72 across the 9 of their papers we have counts for
4 papers · 1 filter
Defense Against Explanation Manipulation
Ruixiang Tang, Ninghao Liu, Fan Yang +2
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications. However,…
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong +4
Recent research has shown Deep Neural Networks (DNNs) to be vulnerable to adversarial examples that induce desired misclassifications in the models. Such risks impede the applicati…
Explainable Recommender Systems via Resolving Learning Representations
Ninghao Liu, Yong Ge, Li Li +3
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing mor…
Adversarial Attacks and Defenses: An Interpretation Perspective
Ninghao Liu, Mengnan Du, Ruocheng Guo +2
Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. A…