A Multi-Head Model for Continual Learning via Out-of-Distribution Replay. - Gyuhak Kim; Bing Liu 001; Zixuan Ke (CoLLAs, 2022)

Continual Learning Based on OOD Detection and Task Masking. - Gyuhak Kim; Sepideh Esmaeilpour; Changnan Xiao; Bing Liu 001 (CVPR Workshops, 2022)

A Theoretical Study on Solving Continual Learning. - Gyuhak Kim; Changnan Xiao; Tatsuya Konishi; Zixuan Ke; Bing Liu 001 (NeurIPS, 2022)

Partially Relaxed Masks for Knowledge Transfer Without Forgetting in Continual Learning. - Tatsuya Konishi; Mori Kurokawa; Chihiro Ono; Zixuan Ke; Gyuhak Kim; Bing Liu 001 (PAKDD (1), 2022)

Continual Pre-training of Language Models. - Zixuan Ke; Yijia Shao; Haowei Lin; Tatsuya Konishi; Gyuhak Kim; Bing Liu 001 (ICLR, 2023)

Learnability and Algorithm for Continual Learning. - Gyuhak Kim; Changnan Xiao; Tatsuya Konishi; Bing Liu 001 (ICML, 2023)

Parameter-Level Soft-Masking for Continual Learning. - Tatsuya Konishi; Mori Kurokawa; Chihiro Ono; Zixuan Ke; Gyuhak Kim; Bing Liu 001 (ICML, 2023)

Continual Learning of Language Models. - Zixuan Ke; Yijia Shao; Haowei Lin; Tatsuya Konishi; Gyuhak Kim; Bing Liu 001 (CoRR, 2023)

Multi-Modal Continual Pre-Training For Audio Encoders. - Gyuhak Kim; Ho-Hsiang Wu; Luca Bondi; Bing Liu 001 (ICASSP, 2024)

Harnessing Business and Media Insights with Large Language Models. - Yujia Bao; Ankit Parag Shah; Neeru Narang; Jonathan Rivers; Rajeev Maksey; Lan Guan; Louise N. Barrere; Shelley Evenson; Rahul Basole; Connie Miao; Ankit Mehta; Fabien Boulay; et al. (CoRR, 2024)

Open-world continual learning: Unifying novelty detection and continual learning. - Gyuhak Kim; Changnan Xiao; Tatsuya Konishi; Zixuan Ke; Bing Liu 001 (Artif. Intell., 2025)

SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models. - Gyuhak Kim; Sumiran Thakur; Su Min Park; Wei Wei; Yujia Bao (CoRR, 2025)