Taehyun Cho - RankMe

18 papers A* 6A 1B 1Journal 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning graph based individual intrinsic reward for multi-agent reinforcement learning. ICT Express Seokhun Ju, Seungyub Han, Taehyun Cho, Jungwoo Lee, Taeyoung Lee, Minkyoung Kim, Jinho An
2026	J	`jnl`	MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction. CoRR Jung Min Lee, Dohyeok Lee, Seokhun Ju, Taehyun Cho, Jin Woo Koo, Li Zhao, Sangwoo Hong, Jungwoo Lee
2025	A*	`conf`	Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation. ICML Taehyun Cho, Seungyub Han, Seokhun Ju, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee
2025	J	`jnl`	Learning Generalizable Visuomotor Policy through Dynamics-Alignment. CoRR Dohyeok Lee, Jung Min Lee, Munkyung Kim, Seokhun Ju, Jin Woo Koo, Kyungjae Lee, Dohyeong Kim, Taehyun Cho, Jungwoo Lee
2025	A*	`conf`	Policy-labeled Preference Learning: Is Preference Enough for RLHF? ICML Taehyun Cho, Seokhun Ju, Seungyub Han, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee
2025	J	`jnl`	Policy-labeled Preference Learning: Is Preference Enough for RLHF? CoRR Taehyun Cho, Seokhun Ju, Seungyub Han, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee
2024	J	`jnl`	On the Convergence of Continual Learning with Adaptive Methods. CoRR Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee
2024	J	`jnl`	SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning. CoRR Dohyeok Lee, Seungyub Han, Taehyun Cho, Jungwoo Lee
2024	A*	`conf`	Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees. NeurIPS Dohyeong Kim, Taehyun Cho, Seungyub Han, Hojun Chung, Kyungjae Lee, Songhwai Oh
2024	J	`jnl`	Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees. CoRR Dohyeong Kim, Taehyun Cho, Seungyub Han, Hojun Chung, Kyungjae Lee, Songhwai Oh
2024	J	`jnl`	Tractable and Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation. CoRR Taehyun Cho, Seungyub Han, Kyungjae Lee, Seokhun Ju, Dohyeong Kim, Jungwoo Lee
2023	A	`conf`	On the Convergence of Continual Learning with Adaptive Methods. UAI Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee
2023	A*	`conf`	Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion. NeurIPS Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee
2023	J	`jnl`	Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion. CoRR Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee
2023	A*	`conf`	SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning. NeurIPS Dohyeok Lee, Seungyub Han, Taehyun Cho, Jungwoo Lee
2021	A*	`conf`	Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication. ICML Sangwoo Hong, Heecheol Yang, YoungSeok Yoon, Taehyun Cho, Jungwoo Lee
2021	J	`jnl`	Optimized Shallow Neural Networks for Sum-Rate Maximization in Energy Harvesting Downlink Multiuser NOMA Systems. IEEE J. Sel. Areas Commun. Heasung Kim, Taehyun Cho, Jungwoo Lee, Wonjae Shin, H. Vincent Poor
2020	B	`conf`	An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels. ISIT Heasung Kim, Taehyun Cho, Jungwoo Lee, Wonjae Shin, H. Vincent Poor

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.