35 citations · 38 across the 3 of their papers we have counts for
4 papers
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Jialu Wang, Yang Liu, Xin Eric Wang
Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bia…
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
Yang Liu, Jialu Wang
In this paper, we answer the question of when inserting label noise (less informative labels) can instead return us more accurate and fair models. We are primarily inspired by thre…
Fair Classification with Group-Dependent Label Noise
Jialu Wang, Yang Liu, Caleb Levy
This work examines how to train fair classifiers in settings where training labels are corrupted with random noise, and where the error rates of corruption depend both on the label…
Linear Classifiers that Encourage Constructive Adaptation
Yatong Chen, Jialu Wang, Yang Liu
Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss i…