2 citations · 5 across the 6 of their papers we have counts for
6 papers
Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach
Wen Huang, Xintao Wu
This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm's reward distribution. A major…
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
Karuna Bhaila, Wen Huang, Yongkai Wu +1
Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protect…
A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias
Huy Mai, Wen Huang, Wei Du +1
The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the tr…
Achieving Counterfactual Fairness for Causal Bandit
Wen Huang, Lu Zhang, Xintao Wu
In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arr…
Achieving User-Side Fairness in Contextual Bandits
Wen Huang, Kevin Labille, Xintao Wu +2
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy base…
Fairness through Equality of Effort
Wen Huang, Yongkai Wu, Lu Zhang +1
Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as…