Uncertainty-based Continual Learning with Adaptive Regularization. - Hongjoon Ahn; Sungmin Cha; Donggyu Lee; Taesup Moon (NeurIPS, 2019)

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (NeurIPS, 2020)

Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty. - Hongjoon Ahn; Taesup Moon (UAI, 2020)

Adaptive Group Sparse Regularization for Continual Learning. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (CoRR, 2020)

A Simple Class Decision Balancing for Incremental Learning. - Hongjoon Ahn; Taesup Moon (CoRR, 2020)

SS-IL: Separated Softmax for Incremental Learning. - Hongjoon Ahn; Jihwan Kwak; Subin Lim; Hyeonsu Bang; Hyojun Kim; Taesup Moon (ICCV, 2021)

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. - Hongjoon Ahn; Yongyi Yang; Quan Gan; Taesup Moon; David P. Wipf (NeurIPS, 2022)

GAN-Based Framework for Unified Estimation of Process-Induced Random Variation in FinFET. - Taeeon Park; Jihwan Kwak; Hongjoon Ahn; Jinwoong Lee; Jaehyuk Lim; Sangho Yu; Changhwan Shin; Taesup Moon (IEEE Access, 2022)

Listwise Reward Estimation for Offline Preference-based Reinforcement Learning. - Heewoong Choi; Sangwon Jung; Hongjoon Ahn; Taesup Moon (ICML, 2024)

Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning. - Hongjoon Ahn; Jinu Hyeon; Youngmin Oh; Bosun Hwang; Taesup Moon (CoRR, 2024)

Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline. - Hongjoon Ahn; Jinu Hyeon; Youngmin Oh; Bosun Hwang; Taesup Moon (ICLR, 2025)

Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning. - Hongjoon Ahn; Heewoong Choi; Jisu Han; Taesup Moon (CoRR, 2025)