Geon-Hyeong Kim

18 papers A* 11C 1Journal 4Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	IRPO: Implicit Policy Regularized Preference Optimization. EACL (Findings) Youngsoo Jang, Yu Jin Kim, Geon-Hyeong Kim, Honglak Lee, Moontae Lee
2025	A*	`conf`	Online Pre-Training for Offline-to-Online Reinforcement Learning. ICML Yongjae Shin, Jeonghye Kim, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngsoo Jang, Geon-Hyeong Kim, Jongseong Chae, Youngchul Sung, Kanghoon Lee, Woohyung Lim
2025	J	`jnl`	SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety. CoRR Geon-Hyeong Kim, Youngsoo Jang, Yu Jin Kim, Byoungjip Kim, Honglak Lee, Kyunghoon Bae, Moontae Lee
2024	A*	`conf`	Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration. ICML Youngsoo Jang, Geon-Hyeong Kim, Byoungjip Kim, Yu Jin Kim, Honglak Lee, Moontae Lee
2024	—	`conf`	Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking. EMNLP (Findings) Byoungjip Kim, Youngsoo Jang, Lajanugen Logeswaran, Geon-Hyeong Kim, Yu Jin Kim, Honglak Lee, Moontae Lee
2023	A*	`conf`	Information-Theoretic State Space Model for Multi-View Reinforcement Learning. ICML HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
2023	A*	`conf`	SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations. NeurIPS Youngsoo Jang, Geon-Hyeong Kim, Jongmin Lee, Sungryull Sohn, Byoungjip Kim, Honglak Lee, Moontae Lee
2022	A*	`conf`	DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations. ICLR Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim
2022	J	`jnl`	LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation. CoRR Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
2022	A*	`conf`	LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. NeurIPS Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
2021	A*	`conf`	Multi-View Representation Learning via Total Correlation Objective. NeurIPS HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
2020	A*	`conf`	Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients. AAAI Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim
2020	A*	`conf`	Variational Inference for Sequential Data with Future Likelihood Estimates. ICML Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
2020	A*	`conf`	Variational Interaction Information Maximization for Cross-domain Disentanglement. NeurIPS HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
2020	J	`jnl`	Variational Interaction Information Maximization for Cross-domain Disentanglement. CoRR HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
2019	J	`jnl`	Bayesian optimistic Kullback-Leibler exploration. Mach. Learn. Kanghoon Lee, Geon-Hyeong Kim, Pedro A. Ortega, Daniel D. Lee, Kee-Eung Kim
2019	C	`conf`	Trust Region Sequential Variational Inference. ACML Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, Kee-Eung Kim
2018	A*	`conf`	Monte-Carlo Tree Search for Constrained POMDPs. NeurIPS Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.