Bae, Heesun

11 publications

NeurIPS 2025 Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models Byeonghu Na, Minsang Park, Gyuwon Sim, Donghyeok Shin, HeeSun Bae, Mina Kang, Se Jung Kwon, Wanmo Kang, Il-chul Moon

ICLR 2025 Distilling Dataset into Neural Field Donghyeok Shin, HeeSun Bae, Gyuwon Sim, Wanmo Kang, Il-chul Moon

NeurIPS 2025 Preference Optimization by Estimating the Ratio of the Data Distribution Yeongmin Kim, HeeSun Bae, Byeonghu Na, Il-chul Moon

ICMLW 2024 DPO-Finetuned Large Multi-Modal Planner with Retrieval-Augmented Generation @ EgoPlan Challenge ICML 2024 Kwanghyeon Lee, Mina Kang, Hyungho Na, HeeSun Bae, Byeonghu Na, Doyun Kwon, Seungjae Shin, Yeongmin Kim, Kim Taewoo, Seungmin Yun, Il-chul Moon

ICLR 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

AAAI 2024 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

ICLR 2024 Unknown Domain Inconsistency Minimization for Domain Generalization Seungjae Shin, HeeSun Bae, Byeonghu Na, Yoon-Yeong Kim, Il-chul Moon

AISTATS 2023 Loss-Curvature Matching for Dataset Selection and Condensation Seungjae Shin, Heesun Bae, Donghyeok Shin, Weonyoung Joo, Il-Chul Moon

ICML 2022 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

ICMLW 2022 Improving Group-Based Robustness and Calibration via Ordered Risk and Confidence Regularization Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, Il-chul Moon