Preprints and Workshop

• (In reviewing) Recent Advances of Multimodal Continual Learning: A Comprehensive Survey. [PDF]
  Yu, D., Zhang, X., Chen, Y., Liu, A.,Zhang, Y., Yu, P.S. and King, I
  IEEE Transactions on Pattern Analysis and Machine Intelligence.
• (In reviewing) Can Watermarked LLMs be identified by Users via Crafted Prompts [PDF]
  Liu, A., Guan, S., Liu, Y., Pan, L., Zhang, Y., Fang, L., Wen, L., Yu, P.S. and Hu, X.
  The International Conference on Learning Representations.
• (IJCAI'19) Additively Homomorphic Encryption-based Deep Neural Network for Asymmetrically Collaborative Machine Learning. [PDF]
  Zhang, Y. and Zhu, H.
  In IJCAI 2019 Workshop on Federated Learning. Solutions have been included in FATE , an industry level open source library for federated learning. See this for detial.

Journals

• (ACM TIST) A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges
  Zhang, Y., Zeng D., Luo J., , Fu X., ,Xu Z., and King, I
  In ACM Transactions on Intelligent Systems and Technology.
• (TKDE) Towards Effective Top-N Hamming Search viaBipartite Graph Contrastive Hashing
  Chen, Y., Fang, Y., Zhang, Y., Ma, C., Hong, Y. and King, I.,
  IEEE Transactions on Knowledge and Data Engineering.

Conference

• (KDD'24) Geometric View of Soft Decorrelation in Self-Supervised Learning[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Chen, Y., Song, Z., Fu. X, Meng, Z., Koniusz, P., and King, I.
  In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
• (IJCAI'24, Survey Track) A Systematic Survey on Federated Semi-supervised Learning[PDF][CODE][BLOG]
  Song, Z., Yang, X.,Zhang, Y., Fu X., Xu, Z., and King, I.
  In 2024 International Joint Conference on Artificial Intelligence.
• (NAACL'24, Findings) Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering[PDF][CODE][BLOG]
  Guo, Z., Zhang, Y., Zhang, Z., Xu, Z., and King, I.
  In 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
• (NeurIPS'23, Spotlight) Mitigating the Popularity Bias in Graph-based Collaborative Filtering[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Chen, Y., Song, Z., Koniusz, P. and King, I.
  In Conference on Neural Information Processing Systems.
• (NeurIPS'23, Spotlight) No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning[PDF][CODE][BLOG]
  Song, Z., Zhang, Y., and King, I.
  In Conference on Neural Information Processing Systems.
• (NeurIPS'23) Optimal Block-wise Asymmetric Graph Construction for Semi-supervised Learning.[PDF][CODE][BLOG]
  Song, Z., Zhang, Y., and King, I.
  In Conference on Neural Information Processing Systems.
• (KDD'23) Contrastive Cross-scale Graph Knowledge Synergy.[PDF][CODE][BLOG]
  Zhang, Y., Chen, Y., Song, Z. and King, I.
  In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
• (SIGIR'23) BWSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering.[PDF]
  Chen, Y., Zhang, Y.,Song. Z., Yang. M., Chen. M., King, I.
  In International ACM SIGIR Conference on Research and Development in Information Retrieval..
• (WWW'23) Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space.[PDF]
  Chen, Y., Fang, Y Zhang, Y., King, I.
  In Proceedings of The Web Conference 2023.
• (AAAI'23, Oral) SFA: Spectral Feature Augmentation for Graph Contrastive Learning.[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
  In AAAI Conference on Artificial Intelligence.
• (AAAI'23) Graph Component Contrastive Learning for Concept Relatedness Estimation[PDF]
  Ma, Y., Song, Z., Hu, X., Li, J. Zhang, Y. and King, I.
  In AAAI Conference on Artificial Intelligence.
• (KDD'22) COSTA: Covariance-Preserved Feature Augmentation for Graph Contrastive Learning.[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
  In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
• (KDD'22) Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification.[PDF]
  Song, Z., Zhang, Y., and King, I.
  In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
• (WWW'22) Graph-adpative Rectified Linear Unit for Graph Neural Networks.[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Song, Z., Koniusz, P. and King, I.
  In Proceedings of The Web Conference 2022.
• (CIKM'21) Semi-supervised Multi-label Learning for Graph-structured Data.[PDF]
  Song, Z., Meng, Z., Zhang, Y., & King, I.
  In Proceedings of International Conference on Information & Knowledge Management.
• (ICASSP'20) Discrete Wasserstein Autoencoders for Document Retrieval.[PDF]
  Zhang, Y., Zhu, H.
  In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing.
• (NAACL'20) .Doc2hash: Learning Discrete Latent variables for Documents Retrieval.[PDF]
  Zhang, Y., Zhu, H.
  In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
• (ICWSM'20) #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 US Presidential Debate.[PDF]
  Rizoiu, M. A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., Xie, L.
  In Twelfth International AAAI Conference on Web and Social Media.