Gwangsu Kim

Proposes a new active learning approach by proposing a new uncertainty measure called the least disagree metric, as well as its efficient estimator, which is proven to be asymptotically consistent. This is then combined with seeding to become a new active learning algorith, LDM-S, which is shown to outperform existing approaches across various architectures and datasets.

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).

Developed least disagree metric (LDM), a new disagreement-based uncertainty measure, and an LDM-based active learning algorithm with state-of-the-art performance guarantee.

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.