From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model. - HeeSun Bae; Seungjae Shin; Byeonghu Na; JoonHo Jang; Kyungwoo Song; Il-Chul Moon (ICML, 2022)

Loss-Curvature Matching for Dataset Selection and Condensation. - Seungjae Shin; HeeSun Bae; DongHyeok Shin; Weonyoung Joo; Il-Chul Moon (AISTATS, 2023)

Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior. - Youngjae Cho; HeeSun Bae; Seungjae Shin; Yeo Dong Youn; Weonyoung Joo; Il-Chul Moon (AAAI, 2024)

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning. - HeeSun Bae; Seungjae Shin; Byeonghu Na; Il-Chul Moon (ICLR, 2024)

Label-Noise Robust Diffusion Models. - Byeonghu Na; Yeongmin Kim; HeeSun Bae; Jung Hyun Lee; Se Jung Kwon; Wanmo Kang; Il-Chul Moon (ICLR, 2024)

Unknown Domain Inconsistency Minimization for Domain Generalization. - Seungjae Shin; HeeSun Bae; Byeonghu Na; Yoon-Yeong Kim; Il-Chul Moon (ICLR, 2024)

Distilling Dataset into Neural Field. - Donghyeok Shin; HeeSun Bae; Gyuwon Sim; Wanmo Kang; Il-Chul Moon (ICLR, 2025)

Preference Optimization by Estimating the Ratio of the Data Distribution. - Yeongmin Kim; HeeSun Bae; Byeonghu Na; Il-Chul Moon (CoRR, 2025)