601 citations · 2k across the 51 of their papers we have counts for
6 papers · 1 filter
A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues
A. R. Sana Ullah, Anupam Das, Anik Das +2
Misinformation during pandemic situations like COVID-19 is growing rapidly on social media and other platforms. This expeditious growth of misinformation creates adverse effects on…
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
Zhao Wang, Kai Shu, Aron Culotta
Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In th…
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
Guoqing Zheng, Giannis Karamanolakis, Kai Shu +1
Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of label…
Labeled Data Generation with Inexact Supervision
Enyan Dai, Kai Shu, Yiwei Sun +1
The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural…
User Preference-aware Fake News Detection
Yingtong Dou, Kai Shu, Congying Xia +2
Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing…
Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features
Tianxiang Zhao, Enyan Dai, Kai Shu +1
Despite the rapid development and great success of machine learning models, extensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias…