1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System
Miru Kim, Mugon Joe, Minhae Kwon
The expansion of machine learning into dynamic environments presents challenges in handling open-world problems where label shift, covariate shift, and unknown classes emerge. Post…
cs.LG2025★ 1 cited
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
Heewon Park, Mugon Joe, Miru Kim +1
In real-world applications, machine learning models face online label shift, where label distributions change over time. Effective adaptation requires careful learning rate selecti…