Publications

Full list:

    1. Towards High-Order Complementary Recommendation via Logical Reasoning Network.
      Longfeng Wu, Yao Zhou, Dawei Zhou.
      IEEE International Conference on Data Mining (ICDM 2022). Short Paper, AC rate = 20%. paper, code
    2. Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.
      Yao Zhou*, Jun Wu*, Haixun Wang, Jingrui He.
      ACM International Conference on Information and Knowledge Management (CIKM 2022). Research track, Full paper, AC rate = 21%. paper, code
    3. From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems.
      Yao Zhou*, Haonan Wang*, Jingrui He, Haixun Wang.
       paper
    4. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network.
      Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He
      ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). Research-track, AC rate = 15.4%. paper, code
    5. Crowd Teaching with Imperfect Labels.
      Yao Zhou*, Arun Reddy Nelakurthy*, Ross Maciejewski, Wei Fan, and Jingrui He.
      International World Wide Web Conference (WWW 2020). Research-track, Oral Presentation, AC rate = 19%. paper, slides, code 
    6. Recent Advances in Machine Teaching: From Machine to Human.
      Yao Zhou, and Jingrui He.
      The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). Tutorial
    7. Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching.
      Yao Zhou, Fenglong Ma, Jing Gao, and Jingrui He.
      ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019). Lecture-style Tutorial
    8. Multi-task Crowdsourcing via an Optimization Framework.
      Yao Zhou, Lei Ying, and Jingrui He.
      ACM Transactions on Knowledge Discovery from Data (TKDD 2019). Full-length Technical Paper
    9. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners.
      Yao Zhou, Arun Reddy Nelakurthi, and Jingrui He.
      ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018). Long Presentation, AC rate = 10.9%. paper, code, slides, poster
    10. A Randomized Approach for Crowdsourcing in the Presence of Multiple Views.
      Yao Zhou and Jingrui He.
      IEEE International Conference on Data Mining (ICDM 2017). Long Paper, AC rate = 9.25%. paper, slides
    11. MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity.
      Yao Zhou, Lei Ying, and Jingrui He.
      SIAM International Conference on Data Mining (SDM 2017). Oral & Poster, AC rate = 26%. paper, slides
    12. Crowdsourcing via Tensor Augmentation and Completion.
      Yao Zhou and Jingrui He.
      25th International Joint Conference on Artificial Intelligence (IJCAI 2016). Oral & Poster, AC rate = 25%. paper, slides, poster
    13. A Practical Method for Counting Arbitrary Target Objects in an Arbitrary Scene.
      Yao Zhou and Jiebo Luo.
      IEEE International Conference on Multimedia & Expo (ICME 2013).
    14. A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems.
      Pengfei Jiang, Weina Wang, Yao Zhou, Jingrui He, and Lei Ying.
      Workshop in ACM SIGMETRICS (NetEcon 2018). paper
  • (* indicates equal contribution)