8 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…
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
Tianyuan Liang, Xuwei Tan, Lei Shi +6
Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive perf…
Individual Fairness In Strategic Classification
Zhiqun Zuo, Mohammad Mahdi Khalili
Strategic classification, where individuals modify their features to influence machine learning (ML) decisions, presents critical fairness challenges. While group fairness in this…
Post-processing for Fair Regression via Explainable SVD
Zhiqun Zuo, Ding Zhu, Mohammad Mahdi Khalili
This paper presents a post-processing algorithm for training fair neural network regression models that satisfy statistical parity, utilizing an explainable singular value decompos…
An Efficient Training Algorithm for Models with Block-wise Sparsity
Ding Zhu, Zhiqun Zuo, Mohammad Mahdi Khalili
Large-scale machine learning (ML) models are increasingly being used in critical domains like education, lending, recruitment, healthcare, criminal justice, etc. However, the train…
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…
Counterfactually Fair Representation
Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang
The use of machine learning models in high-stake applications (e.g., healthcare, lending, college admission) has raised growing concerns due to potential biases against protected s…