5 papers
BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks
Elnaz Nowrouzi, Zhiqun Zuo, Xueru Zhang +1
Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: best-response dynamics may need hundreds of iterations per instance, and…
Demographic-Agnostic Fairness without Harm
Zhongteng Cai, Mohammad Mahdi Khalili, Xueru Zhang
As machine learning (ML) algorithms are increasingly used in social domains to make predictions about humans, there is a growing concern that these algorithms may exhibit biases ag…
Lookahead Counterfactual Fairness
Zhiqun Zuo, Tian Xie, Xuwei Tan +2
As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. \textit{Co…
Learning under Imitative Strategic Behavior with Unforeseeable Outcomes
Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili +1
Machine learning systems have been widely used to make decisions about individuals who may behave strategically to receive favorable outcomes, e.g., they may genuinely improve the…
Federated Learning with Reduced Information Leakage and Computation
Tongxin Yin, Xuwei Tan, Xueru Zhang +2
Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although l…