Dan Qiao - RankMe

23 papers A* 7A 2Journal 13Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Does Flatness imply Generalization for Logistic Loss in Univariate Two-Layer ReLU Network? CoRR Dan Qiao, Yu-Xiang Wang
2025	J	`jnl`	No-Regret Linear Bandits under Gap-Adjusted Misspecification. CoRR Chong Liu, Dan Qiao, Ming Yin, Ilija Bogunovic, Yuxiang Wang
2025	—	`conf`	Rates for Offline Reinforcement Learning with Adaptively Collected Data. L4DC Sunil Madhow, Dan Qiao, Ming Yin, Yuxiang Wang
2025	J	`jnl`	Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon. CoRR Tongtong Liang, Dan Qiao, Yuxiang Wang, Rahul Parhi
2024	A*	`conf`	Differentially Private Reinforcement Learning with Self-Play. NeurIPS Dan Qiao, Yu-Xiang Wang
2024	J	`jnl`	Differentially Private Reinforcement Learning with Self-Play. CoRR Dan Qiao, Yu-Xiang Wang
2024	A*	`conf`	Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. ICML Dan Qiao, Yu-Xiang Wang
2024	J	`jnl`	Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. CoRR Dan Qiao, Yu-Xiang Wang
2024	A*	`conf`	Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. NeurIPS Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang
2024	J	`jnl`	Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. CoRR Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang
2023	A	`conf`	Doubly Fair Dynamic Pricing. AISTATS Jianyu Xu, Dan Qiao, Yu-Xiang Wang
2023	J	`jnl`	Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs. CoRR Dan Qiao, Ming Yin, Yu-Xiang Wang
2023	A*	`conf`	Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. ICLR Dan Qiao, Yu-Xiang Wang
2023	A	`conf`	Near-Optimal Differentially Private Reinforcement Learning. AISTATS Dan Qiao, Yu-Xiang Wang
2023	A*	`conf`	Offline Reinforcement Learning with Differential Privacy. NeurIPS Dan Qiao, Yu-Xiang Wang
2022	A*	`conf`	Differentially Private Linear Sketches: Efficient Implementations and Applications. NeurIPS Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
2022	J	`jnl`	Differentially Private Linear Sketches: Efficient Implementations and Applications. CoRR Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
2022	J	`jnl`	Doubly Fair Dynamic Pricing. CoRR Jianyu Xu, Dan Qiao, Yu-Xiang Wang
2022	J	`jnl`	Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. CoRR Dan Qiao, Yu-Xiang Wang
2022	J	`jnl`	Near-Optimal Differentially Private Reinforcement Learning. CoRR Dan Qiao, Yu-Xiang Wang
2022	J	`jnl`	Offline Reinforcement Learning with Differential Privacy. CoRR Dan Qiao, Yu-Xiang Wang
2022	A*	`conf`	Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. ICML Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang
2022	J	`jnl`	Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. CoRR Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang

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.