Fang-Yi Yu

About

I am an assistant professor at Department of Computer Science at George Mason University. My research is broadly situated at the interface between machine learning, artificial intelligence, and economics. My current research is largely about multi agent system interacting with information: 1) information elicitation and aggregation mechanisms and 2) multi agent learning.

I am looking for Ph.D. students who are interested in these (and other related) problems from theoretical and applied angles. Please apply to Computer Science at George Mason University and include my name in your application. Feel free to reach out to me if you are interested in any of my work.

Working papers

1. Algorithmic Robust Forecast Aggregation
   Yongkang Guo, Jason D. Hartline, Zhihuan Huang, Yuqing Kong, Anant Shah, Fang-Yi Yu.
   

2. Optimal Scoring Rule Design under Partial Knowledge
   Yiling Chen, Fang-Yi Yu.
   

Peer-reviewed papers

1. Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos
   Georgios Piliouras, Fang-Yi Yu.
   The 24th ACM Conference on Economics and Computation (EC 2023)
   

2. Differentially Private Network Data Collection for Influence Maximization
   M. Amin Rahimian, Fang-Yi Yu, Carlos Hurtado.
   The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023).
   

3. Integer Subspace Differential Privacy
   Prathamesh Dharangutte, Jie Gao, Ruobin Gong, Fang-Yi Yu.
   The 37th International Conference on Artificial Intelligence (AAAI 2023).
   

4. Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
   Grant Schoenebeck, Fang-Yi Yu.
   ACM Transactions on Economics and Computation (TEAC 2022)
   The 16th International Conference on Web and Internet Economics (WINE 2020). [slides, poster, video]
   

5. Peer Prediction for Learning Agents
   Shi Feng, Fang-Yi Yu, Yiling Chen.
   The 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
   

6. A System-Level Analysis of Conference Peer Review
   Yichi Zhang, Fang-Yi Yu, Grant Schoenebeck, David Kempe.
   The 23rd ACM Conference on Economics and Computation (EC 2022).
   

7. Optimal Local Bayesian Differential Privacy over Markov Chains
   Darshan Chakrabarti, Jie Gao, Aditya Saraf, Grant Schoenebeck, Fang-Yi Yu.
   The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022).
   

8. Subspace Differential Privacy
   Jie Gao, Ruobin Gong, Fang-Yi Yu.
   The 36th International Conference on Artificial Intelligence (AAAI 2022). [slides, poster]
   

9. The Limits of Multi-task Peer Prediction
   Shuran Zheng, Fang-Yi Yu, Yiling Chen.
   The 22nd ACM Conference on Economics and Computation (EC 2021).
   

10. Cooperation in Threshold Public Projects with Binary Actions
    Yiling Chen, Biaoshuai Tao, Fang-Yi Yu.
    The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).
    

11. Information Elicitation from Rowdy Crowds
    Grant Schoenebeck, Fang-Yi Yu, Yichi Zhang.
    The 30th Web Conference (WWW 2021).
    

12. Timely Information from Prediction Markets
    Grant Schoenebeck, Chenkai Yu, Fang-Yi Yu.
    The 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021).
    

13. Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
    Grant Schoenebeck, Fang-Yi Yu.
    The 12nd Innovations in Theoretical Computer Science Conference (ITCS 2021). [slides, poster]
    

14. Escaping Saddle Points in Constant Dimensional Spaces: an Agent-based Modeling Perspective
    Grant Schoenebeck, Fang-Yi Yu.
    The 21st ACM Conference on Economics and Computation (EC 2020). [slides, short slides, poster, video]
    

15. Limitations of Greed: Influence Maximization in Undirected Networks Re-visited
    Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu.
    The 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2020).
    

16. Information Elicitation Mechanisms for Statistical Estimation
    Yuqing Kong, Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu.
    The 34th International Conference on Artificial Intelligence (AAAI 2020). [poster]
    

17. Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization​​​
    Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu.
    The 23rd International Conference on Randomization and Computation (RANDOM 2019). [slides]
    

18. The Volatility of Weak Ties: Co-evolution of Selection and Influence in Social Networks
    Jie Gao, Grant Schoenebeck, Fang-Yi Yu.
    The 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). [slides]
    

19. Consensus of Interacting Particle Systems on Erdös-Rényi Graphs
    Grant Schoenebeck, Fang-Yi Yu.
    The 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018). [slides]
    

20. Cascades and Myopic Routing in Nonhomogeneous Kleinberg’s Small World Model
    Jie Gao, Grant Schoenebeck, Fang-Yi Yu.
    The 13rd International Conference on Web and Internet Economics (WINE 2017). [slides]
    

21. Engineering Agreement: The Naming Game with Asymmetric and Heterogeneous Agents
    Jie Gao, Bo Li, Grant Schoenebeck, Fang-Yi Yu.
    The 30th International Conference on Artificial Intelligence (AAAI 2017). [slides]
    

22. Complex Contagions on Configuration Model Graphs with a Power-Law Degree Distribution
    Grant Schoenebeck, Fang-Yi Yu.
    The 12nd Conference on Web and Internet Economics (WINE 2016). [slides]
    

23. Sybil Detection Using Latent Network Structure
    Aaron Snook, Grant Schoenebeck, Fang-Yi Yu.
    The 17th ACM Conference on Economics and Computation (EC 2016). [slides]
    

24. General Threshold Model for Social Cascades: Analysis and Simulations
    Jie Gao, Golnaz Ghasemiesfeh, Grant Schoenebeck, Fang-Yi Yu.
    The 17th ACM Conference on Economics and Computation (EC 2016). [slides]
    

Teaching

• CS 688 Machine Learning (Fall 2022, Spring 2023, Spring 2024)

Bio