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Seong Jin Cho

2 accepted papers

2026

TESSAR: Geometry-Aware Active Regression via Dynamic Voronoi Tessellation

ICLR 2026poster

Active learning improves training efficiency by selectively querying the most informative samples for labeling. While it naturally fits classification tasks–where informative samples tend to lie near the decision boundary–its application to regression is less straightforward, as information is distr…

Cited by 0SourceScholar
2024

Querying Easily Flip-flopped Samples for Deep Active Learning

ICLR 2024poster

Active learning, a paradigm within machine learning, aims to select and query unlabeled data to enhance model performance strategically. A crucial selection strategy leverages the model's predictive uncertainty, reflecting the informativeness of a data point. While the sample's distance to the decis…