Zhaomin Wu - RankMe

32 papers A* 6Journal 23Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Mining Intrinsic Rewards from LLM Hidden States for Efficient Best-of-N Sampling. KDD (1) Jizhou Guo, Zhaomin Wu, Hanchen Yang, Philip S. Yu
2026	A*	`conf`	Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures. WWW Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng
2026	J	`jnl`	ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data. CoRR Haodong Zhao, Jinming Hu, Zhaomin Wu, Zongru Wu, Wei Du, Junyi Hou, Caibei Zhao, Zhuosheng Zhang, Bingsheng He, Gongshen Liu
2026	J	`jnl`	Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG. CoRR Yicheng Zhang, Zhen Qin, Zhaomin Wu, Wenqi Zhang, Shuiguang Deng
2025	J	`jnl`	Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts. CoRR Zhaomin Wu, Mingzhe Du, See-Kiong Ng, Bingsheng He
2025	J	`jnl`	Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems. CoRR Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang, Bingsheng He, Gongshen Liu
2025	—	`conf`	Federated Data-Efficient Instruction Tuning for Large Language Models. ACL (Findings) Zhen Qin, Zhaomin Wu, Bingsheng He, Shuiguang Deng
2025	J	`jnl`	LLM DNA: Tracing Model Evolution via Functional Representations. CoRR Zhaomin Wu, Haodong Zhao, Ziyang Wang, Jizhou Guo, Qian Wang, Bingsheng He
2025	J	`jnl`	Learning Relational Tabular Data without Shared Features. CoRR Zhaomin Wu, Shida Wang, Ziyang Wang, Bingsheng He
2025	A*	`conf`	Model-based Large Language Model Customization as Service. EMNLP Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang
2025	J	`jnl`	Reward Inside the Model: A Lightweight Hidden-State Reward Model for LLM's Best-of-N sampling. CoRR Jizhou Guo, Zhaomin Wu, Philip S. Yu
2025	J	`jnl`	Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly. CoRR Zhaomin Wu, Zhen Qin, Junyi Hou, Haodong Zhao, Qinbin Li, Bingsheng He, Lixin Fan
2025	J	`jnl`	WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning. CoRR Zhaomin Wu, Ziyang Wang, Bingsheng He
2024	J	`jnl`	Federated Data-Efficient Instruction Tuning for Large Language Models. CoRR Zhen Qin, Zhaomin Wu, Bingsheng He, Shuiguang Deng
2024	A*	`conf`	Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. NeurIPS Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He
2024	J	`jnl`	Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. CoRR Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He
2024	J	`jnl`	Model-Based Differentially Private Knowledge Transfer for Large Language Models. CoRR Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang
2024	J	`jnl`	Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures. CoRR Yicheng Zhang, Zhen Qin, Zhaomin Wu, Shuiguang Deng
2024	J	`jnl`	Practical Vertical Federated Learning With Unsupervised Representation Learning. IEEE Trans. Big Data Zhaomin Wu, Qinbin Li, Bingsheng He
2024	A*	`conf`	VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. ICLR Zhaomin Wu, Junyi Hou, Bingsheng He
2023	J	`jnl`	A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Trans. Knowl. Data Eng. Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He
2023	J	`jnl`	DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning. Proc. ACM Manag. Data Zhaomin Wu, Junhui Zhu, Qinbin Li, Bingsheng He
2023	—	`conf`	FedTree: A Federated Learning System For Trees. MLSys Qinbin Li, Zhaomin Wu, Yanzheng Cai, Yuxuan Han, Ching Man Yung, Tianyuan Fu, Bingsheng He
2023	J	`jnl`	VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. CoRR Zhaomin Wu, Junyi Hou, Bingsheng He
2022	A*	`conf`	A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. NeurIPS Zhaomin Wu, Qinbin Li, Bingsheng He
2022	J	`jnl`	Practical Vertical Federated Learning with Unsupervised Representation Learning. CoRR Zhaomin Wu, Qinbin Li, Bingsheng He
2022	J	`jnl`	The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems. ACM Trans. Intell. Syst. Technol. Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He
2021	J	`jnl`	A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. CoRR Zhaomin Wu, Qinbin Li, Bingsheng He
2020	A*	`conf`	Privacy-Preserving Gradient Boosting Decision Trees. AAAI Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
2020	J	`jnl`	The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems. CoRR Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He
2019	J	`jnl`	A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. CoRR Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Xu Liu, Bingsheng He
2019	J	`jnl`	Privacy-Preserving Gradient Boosting Decision Trees. CoRR Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He

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.