Yue Tan
Computer Science · The Ohio State University
Publications
42
Citations
1,826
Est. group size
—
Recurring co-author estimate
Active years
32
Publishing since 1995
This researcher's publication record centers on federated learning, a machine learning approach where multiple devices or organizations train a shared model without pooling their raw data, along with related work on graph-based anomaly detection, deep reinforcement learning, and Internet of Things applications. The record also includes some unrelated publications in areas like medicine and operations research, suggesting either interdisciplinary collaborations or possible name overlap with other authors. Prospective students should note the bibliographic data provided shows diverse topics that may not all reflect a single coherent research program.
Publication output rose to a peak around 2020-2021 and has since declined and become more irregular, averaging under 2 publications per year over the most recent five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Relative Error of Scaled Poisson Approximation for Tail Distributions via Stein’s Method
Methodology And Computing In Applied Probability · 2026
- Clinical observation of <italic>Qingdan Granule</italic>(清疸散颗粒) in the treatment of simple obesity with stomach heat syndrome
Lishizhen medicine and materia medica research · 2025
- A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
arXiv (Cornell University) · 2025
- Emerging trends in federated learning: from model fusion to federated X learning
International Journal of Machine Learning and Cybernetics · 2024
- Influence-oriented Personalized Federated Learning
arXiv (Cornell University) · 2024
- Sparse Matching on High-Resolution Maps for Small Object Detection
2024
- Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Proceedings of the AAAI Conference on Artificial Intelligence · 2023
- Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Research Square · 2023
- FedProto: Federated Prototype Learning across Heterogeneous Clients
Proceedings of the AAAI Conference on Artificial Intelligence · 2022
- Federated Learning for Open Banking
arXiv (Cornell University) · 2021
- Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
2021
- How to operate physical showrooms: Service decision and pricing based on product quality
RAIRO - Operations Research · 2021
- Verifying the Relationships among China’s E-Commerce Platform(Pindudu) Traits, Platform Attitude, Product Satisfaction, and Reuse Intention: Focused on the Moderating Role of SNS Utilization
The e-Business Studies · 2021
- Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
arXiv (Cornell University) · 2021
- FedProto: Federated Prototype Learning over Heterogeneous Devices.
arXiv (Cornell University) · 2021
- arXiv (Cornell University)×12
- Lecture notes in electrical engineering×2
- Proceedings of the AAAI Conference on Artificial Intelligence×2
- IEEE Communications Surveys & Tutorials×1
- IEEE Internet of Things Journal×1
- Chenghong Wang
Computer Science · Indiana University
- Hao Wu
Computer Science · Purdue University West Lafayette
- Agnideven Palanisamy Sundar
Computer Science · Purdue University West Lafayette
- Atul Sharma
Computer Science · Purdue University West Lafayette
- Chris Clifton
Computer Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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