Mark Hasegawa-Johnson

Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Proposes a new MMD-based definition of fairness for PCA, then formulate fair PCA as an optimization over the Stiefel manifold. Various theoretical and empirical discussions show the superiority of our approach compared to the existing approach (Olfat & Aswani, AAAI'19).
Junghyun Lee, Gwangsu Kim, Matt Olfat, Mark Hasegawa-Johnson, Chang D. Yoo
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Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Fair Dimensionality Reduction
Part of fair representation learning. Develop a theory of fairness in dimensionality reduction: new definition, new (efficient) algorithm, new theoretical results. Currently focused on PCA.
Junghyun Lee