Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract)


  • Alexandru Lopotenco University of Pennsylvania
  • Ian Tong Pan University of Pennsylvania
  • Jack Zhang University of Pennsylvania
  • Guan Xiong Qiao University of Pennsylvania




Fair Representation Learning, Fairness, MMD, Maximum Mean Discrepancy, PCA, T-SNE


Unsupervised learning methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoding are regularly used in dimensionality reduction within the statistical learning scene. However, despite a pivot toward fairness and explainability in machine learning over the past few years, there have been few rigorous attempts toward a generalized framework of fair and explainable representation learning. Our paper explores the possibility of such a framework that leverages maximum mean discrepancy to remove information derived from a protected class from generated representations. For the optimization, we introduce a binary search component to optimize the Lagrangian coefficients. We present rigorous mathematical analysis and experimental results of our framework applied to t-SNE.




How to Cite

Lopotenco, A., Tong Pan, I., Zhang, J., & Xiong Qiao, G. (2024). Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23567-23568. https://doi.org/10.1609/aaai.v38i21.30476