67 citations · 143 across the 9 of their papers we have counts for
9 papers
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Sina Mohseni, Fan Yang, Shiva Pentyala +6
Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale…
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
Yigeng Zhang, Fan Yang, Yifan Zhang +2
Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…
Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu +4
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such trai…
XDeep: An Interpretation Tool for Deep Neural Networks
Fan Yang, Zijian Zhang, Haofan Wang +2
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the in…
Contextual Local Explanation for Black Box Classifiers
Zijian Zhang, Fan Yang, Haofan Wang +1
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the predi…
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Fan Yang, Mengnan Du, Xia Hu
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are signif…
Learning Credible Deep Neural Networks with Rationale Regularization
Mengnan Du, Ninghao Liu, Fan Yang +1
Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions. It not only hampers their generalization but al…
Fairness in Deep Learning: A Computational Perspective
Mengnan Du, Fan Yang, Na Zou +1
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimina…