Gwangsu Kim

TESSAR: Geometry-Aware Active Regression via Dynamic Voronoi Tessellation
Active learning improves training efficiency by selectively querying the most informative samples for labeling. While it naturally fits …
Cho, Seong Jin, Kim, Gwangsu, Junghyun Lee, Yoon, Hee Suk, Tee, Joshua Tian Jin
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Querying Easily Flip-flopped Samples for Deep Active Learning
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.
Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, Chang D. Yoo
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Querying Easily Flip-flopped Samples for Deep Active Learning
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