128 citations · 718 across the 52 of their papers we have counts for
8 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,…
Modeling Techniques for Machine Learning Fairness: A Survey
Mingyang Wan, Daochen Zha, Ninghao Liu +1
Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minor…
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Kaixiong Zhou, Ninghao Liu, Fan Yang +5
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many d…
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Yushun Dong, Ninghao Liu, Brian Jalaian +1
Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed networks in various web-based applications such as social recommendation and web search. Nevert…
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…
Dynamic Memory based Attention Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu +4
Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and pre…
Sparse-Interest Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao +4
Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical…
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
Fan Yang, Ninghao Liu, Mengnan Du +1
With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current in…