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Found 5 papers, 2 papers with code
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes.
Covariance-Robust Dynamic Watermarking
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 as dynamic watermarking.
Average Margin Regularization for Classifiers
We motivate this regularization by a novel generalization bound that shows a tradeoff in classifier accuracy between maximizing its margin and average margin.
Convex Formulations for Fair Principal Component Analysis
We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.
Spectral Algorithms for Computing Fair Support Vector Machines
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).
