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cs.LG2026
Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling
Yeseul Cho, Baekrok Shin, Changmin Kang +1
Dataset pruning reduces the storage and training costs of deep learning by selecting an informative subset from a large dataset. However, most existing pruning methods require full…
cs.LG2026
AMUSE: Anytime Muon with Stable Gradient Evaluation
Jueun Kim, Baekrok Shin, Jihun Yun +3
Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent works challenge both components: Schedule-Free optimization removes explicit sched…
math.OC2026
Nesterov Acceleration with Operator Decomposition
Jaewook Lee, Ernest K. Ryu, Chulhee Yun
We propose Nesterov acceleration with Operator Decomposition (NOD), which extends Nesterov's accelerated gradient descent (NAG) from smooth strongly convex optimization to the broa…