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cs.LG2025
Towards Reliable Test-Time Adaptation: Style Invariance as a Correctness Likelihood
Gilhyun Nam, Taewon Kim, Joonhyun Jeong +1
Test-time adaptation (TTA) enables efficient adaptation of deployed models, yet it often leads to poorly calibrated predictive uncertainty - a critical issue in high-stakes domains…
cs.LG2025
Learning Flexible Forward Trajectories for Masked Molecular Diffusion
Hyunjin Seo, Taewon Kim, Sihyun Yu +1
Masked diffusion models (MDMs) have achieved notable progress in modeling discrete data, while their potential in molecular generation remains underexplored. In this work, we explo…
cs.LG2024
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
Changhun Kim, Taewon Kim, Seungyeon Woo +2
In real-world scenarios, tabular data often suffer from distribution shifts that threaten the performance of machine learning models. Despite its prevalence and importance, handlin…