56 citations · 73 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
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…
Loss Balancing for Fair Supervised Learning
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
Supervised learning models have been used in various domains such as lending, college admission, face recognition, natural language processing, etc. However, they may inherit pre-e…
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…
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
Mahed Abroshan, Saumitra Mishra, Mohammad Mahdi Khalili
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a…
Fair Sequential Selection Using Supervised Learning Models
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given…
Improving Fairness and Privacy in Selection Problems
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan +1
Supervised learning models have been increasingly used for making decisions about individuals in applications such as hiring, lending, and college admission. These models may inher…
Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms
Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu
Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation…
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu
Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed…