Donghyeok Shin

Ph.D. Student

AAI Lab, KAIST

CV | Google Scholar | |

I am a Ph.D. student in Applied Artificial Intelligence Lab at KAIST ISE, advised by Prof. Il-Chul Moon.

My research interests focus on advancing the efficiency of deep learning across various aspects. I have studied efficiency improvements from both the dataset and network perspectives. Currently, I am interested in enhancing the inference efficiency of generative models.

Contact

tlsehdgur0@kaist.ac.kr
Bldg E2-2, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, 34141

Education

Ph.D. in ISE at KAIST, Present

Advisor: Prof. Il-Chul Moon
M.S. in ISE at KAIST, Feb 2022

Advisor: Prof. Il-Chul Moon

Thesis: Dataset Distillation via Loss Approximation for Continual Learning
B.S. in Mathematical Sciences at KAIST, Feb 2020
B.S. in ISE at KAIST, Feb 2020

Double Major

Latest News

[Jan 2026] 🎉 Three papers are accepted (AMiD, CSD, AC-Sampler) to ICLR 2026.
[Nov 2025] 🏆 Awarded as the Winner of Qualcomm Innovation Fellowship Korea 2025 (QIFK 2025).
[Oct 2025] 🏆 Recognized as the Top Reviewer of NeurIPS 2025.
[Sep 2025] 🎉 One paper is accepted (DATE) to NeurIPS 2025.
[Jan 2025] 🎉 One paper is accepted (DDiF) to ICLR 2025.

Preprints

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

Under review

[ paper / code ]
Towards Adversarially Robust VLMs with an Information-Theoretic Approach

Jiseok Kwak, Donghyeok Shin, Richard Lee Kim, Minsang Park, Zeynep Altiner, and Il-chul Moon

Under review
Towards Pareto-Optimality for Test-Time Adaptation

JoonHo Jang, DongHyeok Shin, Byeonghu Na, HeeSun Bae, and Il-chul Moon

Preprint, 2024.

Publications

(*: Equal contribution)

AMiD: Knowledge Distillation for LLMs with α-mixture Assistant Distribution

Donghyeok Shin, Yeongmin Kim, Suhyeon Jo, Byeonghu Na, and Il-Chul Moon

ICLR 2026

[ paper / code ]
Distillation of Large Language Models via Concrete Score Matching

Yeongmin Kim, Donghyeok Shin, Mina Kang, Byeonghu Na, and Il-Chul Moon

ICLR 2026

[ paper / code ]
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, and Il-chul Moon

ICLR 2026

[ paper / code ]
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

NeurIPS 2025

[ paper / code ]
Distilling Dataset into Neural Field

Donghyeok Shin, HeeSun Bae, Gyuwon Sim, Wanmo Kang, and Il-Chul Moon

ICLR 2025

[ paper / code ]
Diffusion Rejection Sampling

Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, and Il-Chul Moon

ICML 2024

[ paper / code ]
Frequency Domain-based Dataset Distillation

Donghyeok Shin*, Seungjae Shin*, and Il-Chul Moon

NeurIPS 2023

[ paper / code ]
Loss Curvature Matching for Dataset Selection and Condensation

Seungjae Shin*, Heesun Bae*, DongHyeok Shin, Weonyoung Joo, and Il-Chul Moon

AISTATS 2023

[ paper / code ]
Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy

Suhyeon Jo, DongHyeok Shin, Byeonghu Na, JoonHo Jang, and Il-Chul Moon

CIKM 2023

[ paper / code ]
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, and Il-Chul Moon

NeurIPS 2022

[ paper / code ]

Awards & Honors

Winner, Qualcomm Innovation Fellowship Korea 2025 (QIFK 2025)

Distilling Dataset into Neural Field

Top Reviewer, NeurIPS 2025

Academic Activities

Reviewer

International Conference on Learning Representation (ICLR): 2025, 2026
Neural Information Processing Systems (NeurIPS): 2025