Zhaomin Wu

According to our database, Zhaomin Wu authored at least 27 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research.

Qian Wang, Zhongyi Tong, Nuo Chen, Zhaomin Wu, Bingsheng He

CoRR, May, 2026

EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes.

Zhaomin Wu, Jiayi Li, Bingsheng He

CoRR, May, 2026

ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data.

Haodong Zhao, Jinming Hu, Zhaomin Wu, Zongru Wu, Wei Du, Junyi Hou, Caibei Zhao, Zhuosheng Zhang, Bingsheng He, Gongshen Liu

CoRR, March, 2026

Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG.

Yicheng Zhang, Zhen Qin, Zhaomin Wu, Wenqi Zhang, Shuiguang Deng

CoRR, February, 2026

Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures.

Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng

Proceedings of the ACM Web Conference 2026, 2026

Mining Intrinsic Rewards from LLM Hidden States for Efficient Best-of-N Sampling.

Jizhou Guo, Zhaomin Wu, Hanchen Yang, Philip S. Yu

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 2026

2025

LLM DNA: Tracing Model Evolution via Functional Representations.

Zhaomin Wu, Haodong Zhao, Ziyang Wang, Jizhou Guo, Qian Wang, Bingsheng He

CoRR, September, 2025

Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems.

Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang, Bingsheng He, Gongshen Liu

CoRR, September, 2025

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts.

Zhaomin Wu, Mingzhe Du, See-Kiong Ng, Bingsheng He

CoRR, August, 2025

WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning.

Zhaomin Wu, Ziyang Wang, Bingsheng He

CoRR, May, 2025

Reward Inside the Model: A Lightweight Hidden-State Reward Model for LLM's Best-of-N sampling.

Jizhou Guo, Zhaomin Wu, Philip S. Yu

CoRR, May, 2025

Learning Relational Tabular Data without Shared Features.

Zhaomin Wu, Shida Wang, Ziyang Wang, Bingsheng He

CoRR, February, 2025

Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly.

Zhaomin Wu, Zhen Qin, Junyi Hou, Haodong Zhao, Qinbin Li, Bingsheng He, Lixin Fan

CoRR, February, 2025

Model-based Large Language Model Customization as Service.

Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Federated Data-Efficient Instruction Tuning for Large Language Models.

Zhen Qin, Zhaomin Wu, Bingsheng He, Shuiguang Deng

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

Practical Vertical Federated Learning With Unsupervised Representation Learning.

Zhaomin Wu, Qinbin Li, Bingsheng He

IEEE Trans. Big Data, December, 2024

Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures.

Yicheng Zhang, Zhen Qin, Zhaomin Wu, Shuiguang Deng

CoRR, 2024

Model-Based Differentially Private Knowledge Transfer for Large Language Models.

Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang

CoRR, 2024

Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data.

Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks.

Zhaomin Wu, Junyi Hou, Bingsheng He

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He

IEEE Trans. Knowl. Data Eng., April, 2023

DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning.

Zhaomin Wu, Junhui Zhu, Qinbin Li, Bingsheng He

Proc. ACM Manag. Data, 2023

FedTree: A Federated Learning System For Trees.

Qinbin Li, Zhaomin Wu, Yanzheng Cai, Yuxuan Han, Ching Man Yung, Tianyuan Fu, Bingsheng He

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

2022

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems.

Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He

ACM Trans. Intell. Syst. Technol., 2022

A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning.

Zhaomin Wu, Qinbin Li, Bingsheng He

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

Privacy-Preserving Gradient Boosting Decision Trees.

Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Xu Liu, Bingsheng He

CoRR, 2019