9 citations · 10 across the 3 of their papers we have counts for
6 papers
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Junghyun Lee, Gwangsu Kim, Matt Olfat +2
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different pr…
Covariance-Robust Dynamic Watermarking
Matt Olfat, Stephen Sloan, Pedro Hespanhol +3
Attack detection and mitigation strategies for cyberphysical systems (CPS) are an active area of research, and researchers have developed a variety of attack-detection tools such a…
Optimization Hierarchy for Fair Statistical Decision Problems
Anil Aswani, Matt Olfat
Data-driven decision-making has drawn scrutiny from policy makers due to fears of potential discrimination, and a growing literature has begun to develop fair statistical technique…
Average Margin Regularization for Classifiers
Matt Olfat, Anil Aswani
Adversarial robustness has become an important research topic given empirical demonstrations on the lack of robustness of deep neural networks. Unfortunately, recent theoretical re…
Convex Formulations for Fair Principal Component Analysis
Matt Olfat, Anil Aswani
Though there is a growing body of literature on fairness for supervised learning, the problem of incorporating fairness into unsupervised learning has been less well-studied. This…
Spectral Algorithms for Computing Fair Support Vector Machines
Matt Olfat, Anil Aswani
Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is…