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) Low-Rank Adaptation for Foundation Models: A Comprehensive Review [PDF]
  Menglin Yang, Jialin Chen, Yifei Zhang, Jiahong Liu, Jiasheng Zhang, Qiyao Ma, Harshit Verma, Qianru Zhang, Min Zhou, Irwin King, Rex Ying
  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

• (TKDE) Ten Challenging Problems in Federated Foundation Models
  Tao Fan etal. Yifei Zhang, Xiaojin Zhang, Zhenzhe Zheng, and Qiang Yang
  IEEE Transactions on Knowledge and Data Engineering.
• (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.,
  Apart from the first and corresponding authors, authors are listed in alphabetical order by their last names. IEEE Transactions on Knowledge and Data Engineering.

Conference

• (ICCV'25) Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning
  Hung-Chieh Fang, Hsuan-Tien Lin, Irwin King, Yifei Zhang# (# indicate corresponding author)
  International Conference on Computer Vision.
• (ICML'25, Spotlight) Improving Zero-Shot Adversarial Robustness in VLMs by Closed-form Alignment of Adversarial Path Simplices
  Junhao Dong, Piotr Koniusz, Yifei Zhang, Hao Zhu, Weiming Liu, Xinghua Qu, Yew-Soon Ong
  Internal Conference on Machine Learning.
• (ICML'25) Improving Conversational Capabilities of Speech Language Models via Generative Dual-channel Spoken Dialogue Learning
  Qichao Wang, Ziqiao Meng, Wenqian Cui, Yifei Zhang, Pengcheng Wu, Bingzhe Wu, Irwin King, Liang Chen, Peilin Zhao
  Internal Conference on Machine Learning.
• (CVPR'25) pFedMixF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation[PDF][CODE][BLOG]
  Yifei Zhang*, Hao Zhu*, Alysa Ziying Tan, Dianzhi Yu, Longtao Huang, Han Yu (* indicate equal contribution)
  Conference on Computer Vision and Pattern Recognition.
• (CVPR'25) BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning[PDF][CODE][BLOG]
  Hao Zhu*, Yifei Zhang* Junhao Dong, Piotr Koniusz (* indicate equal contribution)
  Conference on Computer Vision and Pattern Recognition.
• (ICLR'25) Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learnin[PDF][CODE][BLOG]
  Aiwei Liu, Sheng Guan, Yiming Liu, Leyi Pan, Yifei Zhang, Liancheng Fang, Lijie Wen, Philip S. Yu, Xuming Hu.
  In International Conference on Learning Representations.
• (KDD'25) Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning[PDF][CODE][BLOG]
  Zhang, Y., Zhu, H., Yang, M., Liu, J., Ying. R, Koniusz, P., and King, I.
  In Proceedings of Sigkdd Conference on Knowledge Discovery and Data Minning.
• (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.