An adaptive approach for determining batch sizes using the hidden Markov model. - Taejong Joo; Minji Seo; Dongmin Shin (J. Intell. Manuf., 2019)

Formalizing Human-Machine Interactions for Adaptive Automation in Smart Manufacturing. - Taejong Joo; Dongmin Shin (IEEE Trans. Hum. Mach. Syst., 2019)

Regularizing activations in neural networks via distribution matching with the Wasserstein metric. - Taejong Joo; Donggu Kang; Byunghoon Kim (ICLR, 2020)

Being Bayesian about Categorical Probability. - Taejong Joo; Uijung Chung; Min-Gwan Seo (ICML, 2020)

Deep Learning Requires Explicit Regularization for Reliable Predictive Probability. - Taejong Joo; Uijung Chung (CoRR, 2020)

IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation. - Taejong Joo; Diego Klabjan (ICML, 2024)

Improving self-training under distribution shifts via anchored confidence with theoretical guarantees. - Taejong Joo; Diego Klabjan (NeurIPS, 2024)

Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context. - Taejong Joo; Diego Klabjan (CoRR, 2025)