15 citations · 31 across the 3 of their papers we have counts for
8 papers
When Optimizing -divergence is Robust with Label Noise
Jiaheng Wei, Yang Liu
We show when maximizing a properly defined -divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. Leveraging its va…
How Do Fair Decisions Fare in Long-term Qualification?
Xueru Zhang, Ruibo Tu, Yang Liu +4
Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamic…
Incentives for Federated Learning: a Hypothesis Elicitation Approach
Yang Liu, Jiaheng Wei
Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources without compromising users' data privacy. The success of a cre…
Distributional Individual Fairness in Clustering
Nihesh Anderson, Suman K. Bera, Syamantak Das +1
In this paper, we initiate the study of fair clustering that ensures distributional similarity among similar individuals. In response to improving fairness in machine learning, rec…
Replication Markets: Results, Lessons, Challenges and Opportunities in AI Replication
Yang Liu, Michael Gordon, Juntao Wang +5
The last decade saw the emergence of systematic large-scale replication projects in the social and behavioral sciences, (Camerer et al., 2016, 2018; Ebersole et al., 2016; Klein et…
Sample Elicitation
Jiaheng Wei, Zuyue Fu, Yang Liu +3
It is important to collect credible training samples for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distributi…