Seungyong Moon

I am a final-year PhD student in Computer Science at Seoul National University advised by Hyun Oh Song. I previously graduated from Seoul National University in 2019 with BS in Mathematics, BA in Economics, and Minor in Computer Science.

Email / CV / Google Scholar / GitHub

Research

My research interests lie in the robustness and generalization of deep neural networks in various domains including computer vision, natural language processing, and reinforcement learning. Specifically, I am interested in

Adversarial Attacks & Robustness
Offline Reinforcement Learning
Generalization in Reinforcement Learning

Publications
	Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning Seungyong Moon, Junyoung Yeom, Bumsoo Park, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2023 code / bibtex Discovering subgoal hierarchies in visually complex, procedurally generated environments poses a significant challenge. We develop a new contrastive learning method along with PPO that successfully unlocks hierarchical achievements in the challenging Crafter benchmark.
	Rethinking Value Function Learning for Generalization in Reinforcement Learning Seungyong Moon, JunYeong Lee, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2022 code / bibtex The value network trained on multiple environments is more likely to memorize the training data and requires sufficient regularization. We develop a novel policy gradient algorithm that improves generalization by reducing the update frequency of the value network.
	Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song International Conference on Machine Learning (ICML), 2022 code / bibtex Crafting adversarial examples against language models is challenging due to their discrete nature and dynamic input sizes. We develop a query-efficient black-bax adversarial attack targeting various language models from RNNs to Transformers via Bayesian optimization.
	Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks Seungyong Moon, Gaon An, Hyun Oh Song AAAI Conference on Artificial Intelligence (AAAI), 2022 code / bibtex By harnessing an intriguing property of deep neural networks that they have robust points in the vicinity of in-distribution data, we propose a new defense framework that preemptively alters images before potential adversarial attacks, making it applicable to realistic scenarios.
	Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2021 code / bibtex The Q-function ensemble technique, originally designed to mitigage overestimation bias in online RL, proves also effective in offline RL with gradient diversification. We develop a new offline RL algorithm that does not require behavior cloning or explicit Q-value penalization.
	Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization Seungyong Moon, Gaon An, Hyun Oh Song International Conference on Machine Learning (ICML), 2019 (Long talk, 159/3424=4.6%) code / bibtex We develop a query-effecient black-box adversarial attack against deep neural networks based on the local search algorithm for non-monotone submodular function maximization, which does not require gradient estimation and becomes free of hyperparameters to tune.

Teaching Experience

Teaching Assistant, Machine Learning (4190.666), Fall 2020, Fall 2022
Teaching Assistant, Introduction to Deep Learning (M2177.0043), Spring 2019
Undergraduate Student Instructor, Basic Calculus 2 (033.017), Fall 2017
Undergraduate Student Instructor, Basic Calculus 1 (033.016), Spring 2017

Work Experience

Research Intern, KRAFTON, Jun 2023 - Sep 2023
Research Intern, DeepMetrics, Jun 2022 - Sep 2022
Reserach Intern, Naver Search & Clova, Jul 2018 - Aug 2018

Honors and Awards

NeurIPS Scholar Award, 2023
NAVER Ph.D. Fellowship Award, 2022
NeurIPS Top Reviewers, 2022
Qualcomm Innovation Fellowship Finalists, 2020, 2022
Yulchon AI Star Scholarship, 2022
KFAS Computer Science Graduate Student Scholarship, 2019 - 2024

Academic Services

Conference Reviewer: ICML (2022, 2023, 2024), NeurIPS (2021, 2022, 2023), ICLR (2024), RLC (2024), AAAI (2022, 2023, 2024)
Journal Reviewer: Neurocomputing (2021), Machine Learning (2023), Transactions on Intelligent Vehicles (2023)

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