Yuhang Liu

Dr Yuhang Liu

Research Fellow (B) (with PhD)

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

Long-Term Objective: The C2C Landscape, Charting Principled Routes from Correlation to Causality through Data Diversity. 

At the heart of my research lies the following foundational questions: 

  • (i) What are the principled intermediate states between correlation and causality? 

  • (ii) How can learning systems progressively traverse these states? 

  • (iii) What concrete benefits do such states already afford?

Short-term Objectives: 

  • Empower Interpretability, Controllability, Reasoning, and Planning in LLMs.

  • Explore Text Diversity to Guide the Discovery of Latent Patterns within Unstructured Data.

  • Develop Fundamental Theories for Representation Learning.

Homepage: https://sites.google.com/view/yuhangliu/homepage

Year Citation
2026 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2026). Identifying Weight-Variant Latent Causal Models. Journal of Machine Learning Research, 27.
2025 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2025). Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. Transactions on Machine Learning Research, 2025-April.
Scopus1
2025 Shu, Y., Liu, Y., Cao, X., Chen, Q., Zhang, B., Zhou, Z., . . . Liu, L. (2025). Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale. Transactions on Machine Learning Research, 2025-June.
2024 Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A. V. D., & Shi, J. Q. (2024). InvariantStock: Learning Invariant Features for Mastering the Shifting Market. Transactions on Machine Learning Research, 2024.
2024 Yan, Q., Wang, H., Ma, Y., Liu, Y., Dong, W., Woźniak, M., & Zhang, Y. (2024). Uncertainty estimation in HDR imaging with Bayesian neural networks. Pattern Recognition, 156, 110802.
DOI Scopus89
2020 Wen, S., Deng, L., & Liu, Y. (2020). Distributed optimization via primal and dual decompositions for delay-constrained FANETs. Ad Hoc Networks, 109, 1-14.
DOI Scopus15
2018 Liu, Y., Dong, W., & Zhou, M. (2018). Frame-Based Variational Bayesian Learning for Independent or Dependent Source Separation. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4983-4996.
DOI Scopus14
2016 Dong, W. Y., Kang, L. L., Liu, Y. H., & Li, K. S. (2016). Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight. Tongxin Xuebao/Journal on Communications, 37(12), 1-10.
DOI Scopus19

Year Citation
2025 Tong, R., Liu, Y., Shi, J. Q., & Gong, D. (2025). Coreset Selection Via Reducible Loss in Continual Learning. In Proceedings of the 13th International Conference on Learning Representations (ICLR 2025) (pp. 57701-57736). Singapore: International Conference on Learning Representations (ICLR).
Scopus8
2025 Zhang, Z., Ng, I., Gong, D., Liu, Y., Gong, M., Huang, B., . . . Shi, J. Q. (2025). ANALYTIC DAG CONSTRAINTS FOR DIFFERENTIABLE DAG LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 63845-63870).
2024 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2024). IDENTIFIABLE LATENT POLYNOMIAL CAUSAL MODELS THROUGH THE LENS OF CHANGE. In 12th International Conference on Learning Representations, ICLR 2024. Online: ICLR.
Scopus9
2024 Cai, Y., Liu, Y., Zhang, Z., & Shi, J. Q. (2024). CLAP: Isolating Content from Style Through Contrastive Learning with Augmented Prompts. In Lecture Notes in computer science Vol. 15079 (pp. 130-147). Milan, Italy: Springer Nature Switzerland.
DOI Scopus5
2022 Yan, Q., Zhang, S., Chen, W., Liu, Y., Zhang, Z., Zhang, Y., . . . Gong, D. (2022). A Lightweight Network for High Dynamic Range Imaging. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 823-831). Online: IEEE.
DOI Scopus13 WoS9
2022 Perez-Pellitero, E., Catley-Chandar, S., Shaw, R., Leonardis, A., Timofte, R., Zhang, Z., . . . Park, C. Y. (2022). NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 1008-1022). Online: IEEE.
DOI Scopus32 WoS15
2022 Yan, Q., Gong, D., Liu, Y., Van Den Hengel, A., & Shi, J. Q. (2022). Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 109-118). Online: IEEE.
DOI Scopus58 WoS43
2022 Zhang, Z., Ng, I., Gong, D., Liu, Y., Abbasnejad, E. M., Gong, M., . . . Shi, J. Q. (2022). Truncated Matrix Power Iteration for Differentiable DAG Learning. In Advances in Neural Information Processing Systems Vol. 35 (pp. 13 pages). Online: Neural information processing systems foundation.
Scopus19
2020 Yang, L., Liu, Y., & Fan, W. (2020). Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12306 LNCS (pp. 17-28). Switzerland: Springer International Publishing.
DOI Scopus2
2019 Liu, Y., Dong, W., Song, W., & Zhang, L. (2019). Bayesian nonnegative matrix factorization with a truncated spike-and-slab prior. In Proceedings - IEEE International Conference on Multimedia and Expo Vol. 2019-July (pp. 1450-1455). online: IEEE.
DOI Scopus2 WoS2
2019 Liu, Y., Dong, W., Zhang, L., Gong, D., & Shi, Q. (2019). Variational bayesian dropout with a hierarchical prior. In Proceedings: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 7117-7126). online: IEEE.
DOI Scopus22 WoS20
2018 Liu, Y., Dong, W., Gong, D., Zhang, L., & Shi, Q. (2018). Deblurring natural image using super-gaussian fields. In Proceedings of the 15th European Conference on Computer Vision as published in Lecture Notes in Computer Science Vol. 11205 LNCS (pp. 467-484). Switzerland: Springer Nature.
DOI Scopus9 WoS19

Date Role Research Topic Program Degree Type Student Load Student Name
2026 Co-Supervisor Personalized Cancer Detection and Treatment via Casual Reinforcement Learning with Hyperspectral Imaging Doctor of Philosophy Doctorate Full Time Mr Meisam Mahmoodi
2026 Co-Supervisor Exploiting Latent Causal Concept Structures in Large Language Models Doctor of Philosophy Doctorate Full Time Mr Shurui Mei
2024 Co-Supervisor Unraveling Opinion Polarization Dynamics in Social Network Echo Chambers: An Graph Modeling Approach with Causality Doctor of Philosophy Doctorate Full Time Mr Wenkang Jiang
2024 Co-Supervisor Unraveling Opinion Polarization Dynamics in Social Network Echo Chambers: An Graph Modeling Approach with Causality Doctor of Philosophy Doctorate Full Time Mr Wenkang Jiang

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