Crowdsourcing via Tensor Augmentation and Completion. - Yao Zhou 003; Jingrui He (IJCAI, 2016)

A Randomized Approach for Crowdsourcing in the Presence of Multiple Views. - Yao Zhou 003; Jingrui He (ICDM, 2017)

MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. - Yao Zhou 003; Lei Ying 001; Jingrui He (SDM, 2017)

Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. - Yao Zhou 003; Arun Reddy Nelakurthi; Jingrui He (KDD, 2018)

A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems. - Pengfei Jiang 001; Weina Wang 001; Yao Zhou 003; Jingrui He; Lei Ying 001 (NetEcon@SIGMETRICS, 2018)

Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. - Yao Zhou 003; Jingrui He (CoRR, 2018)

Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. - Yao Zhou 003; Fenglong Ma; Jing Gao 004; Jingrui He (KDD, 2019)

Multi-task Crowdsourcing via an Optimization Framework. - Yao Zhou 003; Lei Ying 001; Jingrui He (ACM Trans. Knowl. Discov. Data, 2019)

Crowd Teaching with Imperfect Labels. - Yao Zhou 003; Arun Reddy Nelakurthi; Ross Maciejewski; Wei Fan 001; Jingrui He (WWW, 2020)

Robust Decentralized Learning for Neural Networks. - Yao Zhou 003; Jun Wu 019; Jingrui He (CoRR, 2020)

GAN-based Recommendation with Positive-Unlabeled Sampling. - Yao Zhou 003; Jianpeng Xu; Jun Wu 019; Zeinab Taghavi Nasrabadi; Evren Körpeoglu; Kannan Achan; Jingrui He (CoRR, 2020)

PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. - Yao Zhou 003; Jianpeng Xu; Jun Wu 019; Zeinab Taghavi Nasrabadi; Evren Körpeoglu; Kannan Achan; Jingrui He (KDD, 2021)

From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems. - Yao Zhou 003; Haonan Wang; Jingrui He; Haixun Wang (CoRR, 2021)

Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. - Yao Zhou 003; Jun Wu 019; Haixun Wang; Jingrui He (CIKM, 2022)

Towards High-Order Complementary Recommendation via Logical Reasoning Network. - Longfeng Wu; Yao Zhou 003; Dawei Zhou 003 (ICDM, 2022)

Multimodal Federated Learning: A Survey. - Liwei Che; Jiaqi Wang 002; Yao Zhou 003; Fenglong Ma (Sensors, 2023)

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search. - Longfeng Wu; Yao Zhou 003; Tong Zeng; Zhimin Peng; Bhanu Pratap Singh Rawat; Lecheng Zheng; Giovanni Seni; Dawei Zhou 003 (ICDM, 2025)

Collaborative Diffusion Model for Recommender System. - Gyuseok Lee; Yaochen Zhu; Hwanjo Yu; Yao Zhou 003; Jundong Li (WWW (Companion Volume), 2025)

Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs. - Longfeng Wu; Yao Zhou 003; Jian Kang 008; Dawei Zhou 003 (WWW, 2025)

Review-Based Explainable Recommendations: A Transparency Perspective. - Yao Zhou 003; Haonan Wang; Jingrui He; Haixun Wang (Trans. Recomm. Syst., 2025)

Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation. - Yunzhe Qi; Yao Zhou 003; Yikun Ban; Allan Stewart; Chuanwei Ruan; Jiachuan He; Shishir Kumar Prasad; Haixun Wang; Jingrui He (Trans. Mach. Learn. Res., 2026)