35 citations · 154 across the 16 of their papers we have counts for
5 papers · 1 filter
Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)
Yingyao Hu, Yang Liu, Jiaxiong Yao
Latent variable models are crucial in scientific research, where a key variable, such as effort, ability, and belief, is unobserved in the sample but needs to be identified. This p…
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang, Xin Eric Wang, Yang Liu
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias. Their impacts have been largely evaluated for different groups of p…
Metric-Fair Classifier Derandomization
Jimmy Wu, Yatong Chen, Yang Liu
We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier , sample a deterministic classifier $\hat{f}: X \to \{0…
Fairness Transferability Subject to Bounded Distribution Shift
Yatong Chen, Reilly Raab, Jialu Wang +1
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? I…
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Buyun Zhang, Liang Luo, Xi Liu +14
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architec…