Education

PhD [2022.03~Current](Advisor: Taesup Moon)

• Department of Electrical and Computer Engineering, Seoul National University (SNU)

M.S [2019.09~2022.02](Advisor: Taesup Moon)

• Department of Artificial Intelligence, Sungkyunkwan University(SKKU)

B.S [2015.03~2019.08]

• Electrical & Computer Engineering, Sungkyunkwan University(SKKU)

News

[2025.01.23] One paper, "Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline", has been accepted at ICLR 2025.

[2024.05.02] One paper, "Listwise Reward Estimation for Offline Preference-based Reinforcement Learning", has been accepted at ICML 2024.

[2022.09.15] One paper, "Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks", has been accepted at NeurIPS 2022. This is a joint work with Amazon Web Services.

[2021.11.15] I started internship in Amazon Web Services

[2021.07.23] One paper, "SS-IL:Separated Softmax for Incremental Learning", has been accepted at ICCV 2021.

[2020.09.26] One paper, "Continual Learning with Node-Importance based Adaptive Group Sparse Regularization", has been accepted at NeurIPS 2020.

[2020.05.15] One paper, "Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty", has been accepted at UAI 2020.

[2019.09.03] One paper, "Uncertainty-based continual learning with adaptive regularization", has been accepted at NeurIPS 2019.

Preprints

(*=equal contribution)

[P1] Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning

• Hongjoon Ahn*, Heewoong Choi*, Jisu Han*, and Taesup Moon

Publications

Conference paper(*=equal contribution)

[C7] Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline

• Hongjoon Ahn*, Jinu Hyeon*, Youngmin Oh, Bosun Hwang, and Taesup Moon

• International Conference on Learning Representations (ICLR), April 2025

[C6] Listwise Reward Estimation for Offline Preference-based Reinforcement Learning

• Heewoong Choi, Sangwon Jung, Hongjoon Ahn, and Taesup Moon

• Proceedings of International Conference on Machine Learning (ICML), July 2024

[C5] Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

• Hongjoon Ahn, Yongyi Yang, Quan Gan, David Wipf*, and Taesup Moon*

• Proceedings of Neural Information Processing Systems (NeurIPS), December 2022

[C4] SS-IL:Separated Softmax for Incremental Learning

• Hongjoon Ahn*, Jihwan Kwak*, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon

• Proceedings of International Conference on Computer Vision (ICCV), October, 2021

• Preliminary version appeared in CVPR Workshop on Continual Learning in Computer Vision (CLVISION), June 2020

[C3] Continual Learning with Node-Importance based Adaptive Group Sparse Regularization

• Sangwon Jung*, Hongjoon Ahn*, Sungmin Cha and Taesup Moon

• Proceedings of Neural Information Processing Systems (NeurIPS), December 2020

• Preliminary version appeared in CVPR Workshop on Continual Learning in Computer Vision (CLVISION), June 2020

[C2] Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty 

• Hongjoon Ahn and Taesup Moon

• Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), August 2020

[C1] Uncertainty-based Continual Learning with Adaptive Regularization

• Hongjoon Ahn*, Sungmin Cha*, Donggyu Lee and Taesup Moon

• Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

Journal paper

[J1] Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

• Jinwoong Lee, Taeeon Park, Hongjoon Ahn, Jihwan Kwak, Taesup Moon, Changhwan Shin

• Electronics, February 2021

[J2] 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, and Taesup Moon

• IEEE Acess, 2022

Academic Activities

Conference Reviewer

• NeruIPS: 2025, 2024, 2023, 2022

• ICML: 2025, 2024, 2023

• ICLR: 2025, 2024, 2023

• CVPR: 2025, 2024

• CoLLAs: 2023

Journal Reviewer

• TPAMI: 2023, 2022

Work Experience

[2021.11.15~2022.11.15] Amazon Web Services (AWS)

• Applied Scientist internship

• Mentor: David Wipf

• Working with Amazon AI Research team in Shanghai

Awards and Scholarship

[2023.01] BK21 Fellowship

[2022.07] Youlchon AI Star Fellowship

[2019.09~2021.08] Scholarship for graduated school student (SKKU)

[2020.06~2021.12] Kwanjeong Scholarship (for M.S degree)

[2020.09] Qualcomm Innovation Fellowship Korea

• Awarded for the "Continual Learning with Node-Importance based Adaptive Group Sparse Regularization"