Learning to Generalize to Unseen Tasks with Bilevel Optimization. - Hayeon Lee; Donghyun Na; Haebeom Lee; Sung Ju Hwang (CoRR, 2019)

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks. - Haebeom Lee; Hayeon Lee; Donghyun Na; Saehoon Kim; Minseop Park; Eunho Yang; Sung Ju Hwang (ICLR, 2020)

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets. - Hayeon Lee; Eunyoung Hyung; Sung Ju Hwang (ICLR, 2021)

FLUID-XP: flexible user interface distribution for cross-platform experience. - Sunjae Lee; Hayeon Lee; Hoyoung Kim; Sangmin Lee; Jeong Woon Choi; Yuseung Lee; Seono Lee; Ahyeon Kim; Jean Y. Song; Sangeun Oh; Steven Y. Ko; Insik Shin (MobiCom, 2021)

Task-Adaptive Neural Network Search with Meta-Contrastive Learning. - Wonyong Jeong; Hayeon Lee; Geon Park; Eunyoung Hyung; Jinheon Baek; Sung Ju Hwang (NeurIPS, 2021)

Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning. - Hayeon Lee; Sewoong Lee; Song Chong; Sung Ju Hwang (NeurIPS, 2021)

Task-Adaptive Neural Network Retrieval with Meta-Contrastive Learning. - Wonyong Jeong; Hayeon Lee; Gun Park; Eunyoung Hyung; Jinheon Baek; Sung Ju Hwang (CoRR, 2021)

HELP: Hardware-Adaptive Efficient Latency Predictor for NAS via Meta-Learning. - Hayeon Lee; Sewoong Lee; Song Chong; Sung Ju Hwang (CoRR, 2021)

Online Hyperparameter Meta-Learning with Hypergradient Distillation. - Haebeom Lee; Hayeon Lee; Jaewoong Shin; Eunho Yang; Timothy M. Hospedales; Sung Ju Hwang (ICLR, 2022)

SuperNet in Neural Architecture Search: A Taxonomic Survey. - Stephen Cha; Taehyeon Kim 001; Hayeon Lee; Se-Young Yun (CoRR, 2022)

A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models. - Hayeon Lee; Rui Hou 007; Jongpil Kim; Davis Liang; Sung Ju Hwang; Alexander Min (ACL (Findings), 2023)

Co-training and Co-distillation for Quality Improvement and Compression of Language Models. - Hayeon Lee; Rui Hou 007; Jongpil Kim; Davis Liang; Hongbo Zhang; Sung Ju Hwang; Alexander Min (EMNLP (Findings), 2023)

Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets. - Hayeon Lee; Sohyun An; Minseon Kim; Sung Ju Hwang (ICLR, 2023)

DiffusionNAG: Task-guided Neural Architecture Generation with Diffusion Models. - Sohyun An; Hayeon Lee; Jaehyeong Jo; Seanie Lee; Sung Ju Hwang (CoRR, 2023)

Disentangled Knowledge Distillation for Unified Multi-Class Anomaly Detection. - Jiyong Jang; Hayeon Lee; Younkwan Lee (ICIP, 2024)

DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models. - Sohyun An; Hayeon Lee; Jaehyeong Jo; Seanie Lee; Sung Ju Hwang (ICLR, 2024)

Diffusion-based Neural Network Weights Generation. - Bedionita Soro; Bruno Andreis; Hayeon Lee; Song Chong; Frank Hutter; Sung Ju Hwang (CoRR, 2024)

Diffusion-based Neural Network Weights Generation. - Bedionita Soro; Bruno Andreis; Hayeon Lee; Wonyong Jeong; Song Chong; Frank Hutter; Sung Ju Hwang (ICLR, 2025)

Efficient and robust 3D blind harmonization for large domain gaps. - Hwihun Jeong; Hayeon Lee; Se Young Chun; Jongho Lee (CoRR, 2025)

Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset. - Kasra Moazzami; Seoyoun Son; John Lin; Sun Min Lee; Daniel Son; Hayeon Lee; Jeongho Lee; Seongji Lee (CoRR, 2025)