6 citations · 11 across the 4 of their papers we have counts for
6 papers
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili +2
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in man…
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…
Recycled ADMM: Improve Privacy and Accuracy with Less Computation in 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…