Fuyuan Lyu

McGill University

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I’m currently a Ph.D. candidate at McGill University and Mila. I am fortunate to be advised by Prof Jin L.C. Guo and Prof Xue Liu.

Before joining McGill, I got my bachelor’s degree in Computer Science and Zhiyuan Honour Degree in Engineering from Shanghai Jiao Tong University(SJTU). I was advised by Prof Li Jiang for my Bachelor’s thesis and worked with Prof Xiaokang Yang. I also worked with Prof Weichen Liu as a visiting research student at Nanyang Technological University(NTU), who inspired me to conduct high-quality research.

I am passionate about building Data-Centric AI. Specificially, I am studying (i) how to model and select feature and (ii) how to provide high-quality labels for both deep learning and foundation models fully automaticially. I am eager to free humankind from repetitive, tedious work and enable us to focus on more creative tasks. I am actively working on its application in various domains, including advertisement, finance, and software engineering.

Email:

  • FIRST_NAME DOT LAST_NAME AT MAIL DOT MCGILL DOT CA
  • FIRST_NAME DOT LAST_NAME AT MILA DOT QUEBEC

news

Apr 8, 2026 I was invited to give a podcast on promptable recommendation at Data Skeptic. Thanks for the invite, Kyle!
Apr 7, 2026 Two papers got accepted by ACL 2026
Apr 3, 2026 One paper got accepted by SIGIR 2026
Mar 30, 2026 Our latest survey on agent communication protocols is available. Feel free to check!
May 16, 2025 Two papers got accepted by KDD 2025

selected publications

2026

  1. CoRR
    Beyond Message Passing: A Semantic View of Agent Communication Protocols
    Dun Yuan, Fuyuan Lyu, Ye Yuan, Weixu Zhang, Bowei He, Jiayi Geng, Linfeng Du, Zipeng Sun, Yankai Chen, Changjiang Han, and 4 more authors
    In CoRR, 2026
    arXiv Website
  2. CoRR
    Give Users the Wheel: Towards Promptable Recommendation Paradigm
    Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang, Yupeng Hou, Haolun Wu, Xing Tang, Xue Liu, Jin L.C. Guo, and Xiuqiang He
    In CoRR, 2026
    arXiv
  3. SIGIR
    Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation
    Fuyuan Lyu, Zhentai Chen, Jingyan Jiang, Lingjie Li, Xing Tang, Xiuqiang He, and Xue Liu
    In SIGIR, 2026
    arXiv Code

2025

  1. KDD
    Timing is important: Risk-aware Fund Allocation based on Time-Series Forecasting
    Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying, Zhiyan Xu, Wen Zou, Haolun Wu, Xiuqiang He, and Xing Tang
    In KDD, 2025
    arXiv Code Proceeding Poster Slides
  2. ACL
    Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge
    Qiyuan Zhang, Yufei Wang, Yuxin Jiang, Liangyou Li, Chuhan Wu, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Fuyuan Lyu, and 1 more author
    In ACL, 2025
    arXiv Code Proceeding
  3. CoRR
    A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
    Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Wenyue Hua, Haolun Wu, Zhihan Guo, Yufei Wang, Niklas Muennighoff, and 3 more authors
    In CoRR, 2025
    arXiv Code Slides Video Website
  4. ICLR
    RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
    Qiyuan Zhang, Yufei Wang, Tiezheng Yu, Yuxin Jiang, Chuhan Wu, Liangyou Li, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, and 2 more authors
    In ICLR, 2025
    arXiv Proceeding

2024

  1. EMNLP
    Collaborative Performance Prediction for Large Language Models
    Qiyuan Zhang, Fuyuan Lyu, Xue Liu, and Chen Ma
    In EMNLP, 2024
    arXiv Code Proceeding Poster Slides Video

2023

  1. NeurIPS
    Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
    Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Xiuqiang He, and Xue Liu
    In NeurIPS, 2023
    arXiv Code Slides
  2. WWW
    Optimizing Feature Set for Click-Through Rate Prediction
    Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Liang, Xiuqiang He, and Xue Liu
    In WWW, 2023
    arXiv Code Proceeding Slides Video

2022

  1. CIKM
    Learning Optimal Embedding Table for Click-through Rate Prediction
    Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, and Xue Liu
    In CIKM, 2022
    arXiv Code Proceeding Slides
  2. ICDE
    Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction
    Fuyuan Lyu, Xing Tang, Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, and Xue Liu
    In ICDE, 2022
    arXiv Code Proceeding Poster Slides Video