5 citations · 9 across the 3 of their papers we have counts for
4 papers
Toward Annotator Group Bias in Crowdsourcing
Haochen Liu, Joseph Thekinen, Sinem Mollaoglu +5
Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations…
Trustworthy AI: A Computational Perspective
Haochen Liu, Yiqi Wang, Wenqi Fan +6
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human soci…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
Haochen Liu, Wei Jin, Hamid Karimi +2
It is evident that deep text classification models trained on human data could be biased. In particular, they produce biased outcomes for texts that explicitly include identity ter…