Generative Models
Diffusion-based Generative Models, Variational Autoencoders, Large Language Models
[1, 6, 11, 12, 13, 16, 17, 18, 19, 20]
Incomplete Dataset
Noisy Label Learning, Positive-Unlabeled Learning, Distribution Shift
[1, 2, 3, 5, 7, 8, 9, 10, 11, 12]
Domain-Specific Tasks
Scene Text Recognition in CV, Multi-modal Planner in Robotics, Relation Extraction in NLP
[4, 14, 15]
International Conference
[20] Training-free Safe Text Embedding Guidance for Text-to-Image Diffusion Models (STG)
Byeonghu Na, Mina Kang, Jiseok Kwak, Minsang Park, Jiwoo Shin, SeJoon Jun, Gayoung Lee, Jin-Hwa Kim, and Il-Chul Moon
NeurIPS 2025 (The Thirty-ninth Annual Conference on Neural Information Processing Systems)
[19] Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models (DATE)
Byeonghu Na, Minsang Park, Gyuwon Sim, Donghyeok Shin, HeeSun Bae, Mina Kang, Se Jung Kwon, Wanmo Kang, and Il-Chul Moon
NeurIPS 2025 (The Thirty-ninth Annual Conference on Neural Information Processing Systems)
[18] Preference Optimization by Estimating the Ratio of the Data Distribution (BPO)
Yeongmin Kim, HeeSun Bae, Byeonghu Na, and Il-Chul Moon
NeurIPS 2025 (The Thirty-ninth Annual Conference on Neural Information Processing Systems)
[17] Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
Jiwoo Shin, Byeonghu Na, Mina Kang, Wonhyeok Choi, and Il-Chul Moon
GenProCC Workshop @ NeurIPS 2025 (Generative and Protective AI for Content Creation)
[16] Diffusion Bridge AutoEncoders for Unsupervised Representation Learning (DBAE)
Yeongmin Kim, Kwanghyeon Lee, Minsang Park, Byeonghu Na, and Il-Chul Moon
ICLR 2025 (The Thirteenth International Conference on Learning Representations) [Spotlight]
[15] Reward-based Input Construction for Cross-document Relation Extraction (REIC)
Byeonghu Na*, Suhyeon Jo*, Yeongmin Kim, and Il-Chul Moon
ACL 2024 (The 62nd Annual Meeting of the Association for Computational Linguistics) [Oral]
[14] 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, Taewoo Kim, Seungmin Yun, and Il-Chul Moon
The Multi-modal Foundation Model meets Embodied AI Workshop @ ICML 2024
[13] Diffusion Rejection Sampling (DiffRS)
Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, and Il-Chul Moon
ICML 2024 (The Forty-first International Conference on Machine Learning)
[12] Label-Noise Robust Diffusion Models (TDSM)
Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, and Il-Chul Moon
ICLR 2024 (The Twelfth International Conference on Learning Representations)
[11] Training Unbiased Diffusion Models From Biased Dataset (TIW-DSM)
Yeongmin Kim, Byeonghu Na, JoonHo Jang, Minsang Park, Dongjun Kim, Wanmo Kang, and Il-Chul Moon
ICLR 2024 (The Twelfth International Conference on Learning Representations)
[10] Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning (RENT)
HeeSun Bae, Seungjae Shin, Byeonghu Na, and Il-Chul Moon
ICLR 2024 (The Twelfth International Conference on Learning Representations)
[9] Unknown Domain Inconsistency Minimization for Domain Generalization (UDIM)
Seungjae Shin, Heesun Bae, Byeonghu Na, Yoon-Yeong Kim, and Il-Chul Moon
ICLR 2024 (The Twelfth International Conference on Learning Representations)
[8] Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy (HUPM)
Suhyeon Jo, DongHyeok Shin, Byeonghu Na, JoonHo Jang, Il-Chul Moon
CIKM 2023 (The 32nd ACM International Conference on Information and Knowledge Management)
[7] SAAL: Sharpness-Aware Active Learning (SAAL)
Yoon-Yeong Kim*, Youngjae Cho*, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon
ICML 2023 (The 40th International Conference on Machine Learning)
[6] Maximum Likelihood Training of Implicit Nonlinear Diffusion Models (INDM)
Dongjun Kim*, Byeonghu Na*, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, and Il-Chul Moon
NeurIPS 2022 (The Conference on Neural Information Processing Systems)
[5] Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation (UADAL)
JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, and Il-Chull Moon
NeurIPS 2022 (The Conference on Neural Information Processing Systems)
[4] Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features (MATRN)
Byeonghu Na, Yoonsik Kim, and Sungrae Park
ECCV 2022 (European Conference on Computer Vision)
[3] Improving Group-based Robustness and Calibration via Ordered Risk and Confidence Regularization (ORC)
Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, and Il-Chul Moon
SCIS Workshop in ICML 2022 (The Workshop on Spurious Correlations, Invariance, and Stability, International Conference on Machine Learning)
[2] From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model (NPC)
HeeSun Bae*, Seungjae Shin*, Byeonghu Na, JoonHo Jang, Kyungwoo Song, and Il-Chul Moon
ICML 2022 (The 39th International Conference on Machine Learning)
[1] Deep Generative Positive-Unlabeled Learning under Selection Bias (VAE-PU)
Byeonghu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoon-Yeong Kim, and Il-Chul Moon
CIKM 2020 (The 29th ACM International Conference on Information and Knowledge Management)
Domestic Conference
[D1] Simultaneous execution model development based on Artificial neural network (Topic modeling and article classification on news data)
Seungjae Shin, Byeonghu Na, Donghyeok Shin, Yeongyeon Na
KSC 2017 (The Korea Software Congress 2017)