Jeyoon Yeom — accepted AI-conference papers

2025

Flat Posterior Does Matter For Bayesian Model Averaging

Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung +2

UAI 2025

Bayesian neural networks (BNNs) estimate the posterior distribution of model parameters and utilize posterior samples for Bayesian Model Averaging (BMA) in prediction. However, despite the crucial role of flatness in the loss landscape in improving the generalization of neural networks, its impact o

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2024

CED: Comparing Embedding Differences for Detecting Out-of-Distribution and Hallucinated Text

Hakyung Lee, Keon-Hee Park, Hoyoon Byun, Jeyoon Yeom, Jihee Kim, Gyeong-Moon Park +1

EMNLP 2024finding

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety and robustness of models deployed in real-world scenarios. While most studies on OOD detection focus on fine-tuned models trained on in-distribution (ID) data, detecting OOD in pre-trained models is also important due to…

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