Fairness

Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk my recent work (submitted to NeurIPS) on a streaming variant of fair PCA. Abstract Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another.

Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will talk about my own paper “Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold” (Lee et al.

Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about my recent preprint “Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold” (Lee et al.

Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared to the existing approaches.

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).

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.