8 citations · 9 across the 6 of their papers we have counts for
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cs.LG2021
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Joonhyung Park, Hajin Shim, Eunho Yang
Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this…
cs.LG2021
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Yeonsung Jung, Hajin Shim, June Yong Yang +1
Deep neural networks (DNNs), despite their impressive ability to generalize over-capacity networks, often rely heavily on malignant bias as shortcuts instead of task-related inform…
cs.LG2017★ 8 cited
Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification
Hajin Shim, Sung Ju Hwang, Eunho Yang
We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-e…