Best-of-$N$ Guidance for Test-time Diffusion Alignment

Richard Lee Kim, Yeongmin Kim, Gyuwon Sim, Taekyu Kim, Minsang Park, and Il-Chul Moon , 2026 , The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026) , Dec 6-12, 2026. , Sydney , Australia

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

Byeonghu Na, Donghyeok Shin, Yeongmin Kim, Mina Kang, and Il-Chul Moon , 2026 , 12 , The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026) , Dec 6-12, 2026. , Sydney , Australia

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models

Yeongmin Kim, Donghyeok Shin, Byeonghu Na, Minsang Park, Richard Lee Kim, and Il-Chul Moon , 2026 , Jul , International Conference on Machine Learning (ICML 2026) , Jul 6-11 , Seoul , Korea

Distillation of Large Language Models via Concrete Score Matching

Yeongmin Kim, Donghyeok Shin, Mina Kang, Byeonghu Na, and Il-Chul Moon , 2026 , International Conference on Learning Representations (ICLR 2026) , April 23-27 , Rio de Janeiro , Brazil

AC-Sampler: Accelerate and Correct Diffusion Sampling with Metropolis-Hastings Algorithm

Minsang Park, Gyuwon Sim, Hyungho Na, Jiseok Kwak, Sumin Lee, Richard Lee Kim, Donghyeok Shin, Byeonghu Na, Yeongmin Kim, Il-chul Moon , 2026 , International Conference on Learning Representations (ICLR 2026) , April 23-27 , Rio de Janeiro , Brazil

Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment

Byeonghu Na, Hyungho Na, Yeongmin Kim, Suhyeon Jo, HeeSun Bae, Mina Kang, and Il-Chul Moon , 2026 , International Conference on Learning Representations (ICLR 2026) , April 23-27 , Rio de Janeiro , Brazil

AMiD: Knowledge Distillation for LLMs α-mixture Assistant Distribution

Donghyeok Shin, Yeongmin Kim, Suhyeon Jo, Byeonghu Na, and Il-Chul Moon , 2026 , International Conference on Learning Representations (ICLR 2026) , April 23-27 , Rio de Janeiro , Brazil

Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models

Byeonghu Na, Mina Kang, Jiseok Kwak, Minsang Park, Jiwoo Shin, SeJoon Jun, Gayoung Lee, Jin-Hwa Kim, and Il-Chul Moon , 2025 , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA

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, and Il-Chul Moon , 2025 , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA

Preference Optimization by Estimating the Ratio of the Data Distribution

Yeongmin Kim, Heesun Bae, Byeonghu Na, and Il-Chul Moon , 2005 , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA , Neural Information Processing Systems (NeurIPS 2025) , Dec 2 , San Diego , USA

Diffusion Bridge AutoEncoders for Unsupervised Representation Learning

Yeongmin Kim, Kwanghyeon Lee, Minsang Park, Byeonghu Na, and Il-Chul Moon , 2025 , Apr , International Conference on Learning Representations (ICLR 2025) , Apr 24-28 , Singapore , Singapore

Trajectory-Class-Aware Multi-Agent Reinforcement Learning

Hyungho Na, Kwanghyeon Lee, Sumin Lee, and Il-Chul Moon , 2025 , Apr , International Conference on Learning Representations (ICLR 2025) , Apr 24-28 , Singapore , Singapore

Distilling Dataset into Neural Field

Donghyeok Shin, HeeSun Bae, Gyuwon Sim, Wanmo Kang, and Il-Chul Moon , 2025 , 4 , International Conference on Learning Representations (ICLR 2025) , Apr. 24-28 , Singapore , Singapore

Reward-based Input Construction for Cross-document Relation Extraction

Byeonghu Na, Suhyeon Jo, Yeongmin Kim, and Il-Chul Moon , 2024 , Aug , Annual Meeting of the Association for Computational Linguistics (ACL 2024) , Aug 11-16 , Bangkok , Thailand

Diffusion Rejection Sampling

Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, and Il-Chul Moon , 2024 , International Conference on Machine Learning (ICML 2024) , Jul 21-27 , Vienna , Austria