| Multimodal federated learning: A survey L Che, J Wang, Y Zhou, F Ma Sensors 23 (15), 6986, 2023 | 128 | 2023 |
| PURE: Positive-unlabeled recommendation with generative adversarial network Y Zhou, J Xu, J Wu, Z Taghavi, E Korpeoglu, K Achan, J He ACM International Conference on Knowledge Discovery & Data Mining (KDD …, 2021 | 64* | 2021 |
| Crowdsourcing via tensor augmentation and completion Y Zhou, J He International Joint Conference on Artificial Intelligence (IJCAI), 2435-2441, 2016 | 58 | 2016 |
| Adversarial robustness through bias variance decomposition: A new perspective for federated learning Y Zhou, J Wu, H Wang, J He ACM international conference on information & knowledge management (CIKM …, 2022 | 44* | 2022 |
| Unlearn what you have learned: Adaptive crowd teaching with exponentially decayed memory learners Y Zhou, AR Nelakurthi, J He ACM International Conference on Knowledge Discovery & Data Mining (KDD …, 2018 | 42 | 2018 |
| MultiC2: An optimization framework for learning from task and worker dual heterogeneity Y Zhou, L Ying, J He SIAM International Conference on Data Mining (SDM), 579-587, 2017 | 34 | 2017 |
| Review-based explainable recommendations: A transparency perspective Y Zhou, H Wang, J He, H Wang ACM Transactions on Recommender Systems, 2024 | 31* | 2024 |
| A randomized approach for crowdsourcing in the presence of multiple views Y Zhou, J He IEEE International Conference on Data Mining (ICDM), 685-694, 2017 | 24 | 2017 |
| Multi-task crowdsourcing via an optimization framework Y Zhou, L Ying, J He ACM Transactions on Knowledge Discovery from Data (TKDD) 13 (3), 1-26, 2019 | 21 | 2019 |
| Towards high-order complementary recommendation via logical reasoning network L Wu, Y Zhou, D Zhou 2022 IEEE International Conference on Data Mining (ICDM), 1227-1232, 2022 | 19 | 2022 |
| A practical method for counting arbitrary target objects in arbitrary scenes Y Zhou, J Luo IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2013 | 17 | 2013 |
| Crowd teaching with imperfect labels Y Zhou, AR Nelakurthi, R Maciejewski, W Fan, J He ACM The Web Conference (WWW), 110-121, 2020 | 15 | 2020 |
| Optimizing the wisdom of the crowd: Inference, learning, and teaching Y Zhou, F Ma, J Gao, J He ACM International Conference on Knowledge Discovery & Data Mining (KDD …, 2019 | 14 | 2019 |
| Side information-driven session-based recommendation: A survey X Zhang, B Xu, C Li, Y Zhou, L Li, H Lin arXiv preprint arXiv:2402.17129, 2024 | 6 | 2024 |
| A winners-take-all incentive mechanism for crowd-powered systems P Jiang, W Wang, Y Zhou, J He, L Ying ACM International Conference on Measurement and Modeling of Computer Systems …, 2018 | 4 | 2018 |
| Collaborative diffusion model for recommender system G Lee, Y Zhu, H Yu, Y Zhou, J Li ACM The Web Conference (WWW), 1091-1095, 2025 | 3 | 2025 |
| Bridging fairness and uncertainty: Theoretical insights and practical strategies for equalized coverage in GNNs L Wu, Y Zhou, J Kang, D Zhou ACM The Web Conference (WWW), 4625-4634, 2025 | 3 | 2025 |
| Optimizing the wisdom of the crowd: Learning, teaching, and recommendation Y Zhou University of Illinois Urbana Champaign, 2022 | | 2022 |
Jingrui HeUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
