XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning. - Sung Whan Yoon; Do-Yeon Kim 001; Jun Seo; Jaekyun Moon (ICML, 2020)

Few-Round Learning for Federated Learning. - Younghyun Park; Dong-Jun Han; Do-Yeon Kim 001; Jun Seo; Jaekyun Moon (NeurIPS, 2021)

Deep Neural Network Compression for Image Inpainting. - Soyeong Kim; Do-Yeon Kim 001; Jaekyun Moon (ECCV Workshops (7), 2022)

Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning. - Do-Yeon Kim 001; Dong-Jun Han; Jun Seo; Jaekyun Moon (ICLR, 2023)

SplitGP: Achieving Both Generalization and Personalization in Federated Learning. - Dong-Jun Han; Do-Yeon Kim 001; Minseok Choi; Christopher G. Brinton; Jaekyun Moon (INFOCOM, 2023)

EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning. - Mohammad Mahdi Rahimi; Hasnain Irshad Bhatti; Younghyun Park; Humaira Kousar; Do-Yeon Kim 001; Jaekyun Moon (NeurIPS, 2023)

Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning. - Do-Yeon Kim 001; Dong-Jun Han; Jun Seo; Jaekyun Moon (ICML, 2024)

Federated Split Learning With Joint Personalization-Generalization for Inference-Stage Optimization in Wireless Edge Networks. - Dong-Jun Han; Do-Yeon Kim 001; Minseok Choi; David Nickel; Jaekyun Moon; Mung Chiang; Christopher G. Brinton (IEEE Trans. Mob. Comput., 2024)

Adaptive Energy Alignment for Accelerating Test-Time Adaptation. - Wonjeong Choi; Do-Yeon Kim 001; Jungwuk Park; Jungmoon Lee; Younghyun Park; Dong-Jun Han; Jaekyun Moon (ICLR, 2025)