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Yuanbang Liang
(he/him)
Teams and roles for Yuanbang Liang
Research Associate
Publication
2026
- Wang, Y. et al., 2026. GRADE: probing knowledge gaps in LLMs through gradient subspace dynamics. Presented at: COLM 2026: The Third Annual Conference on Language Modeling San Francisco, California 6 - 9 October 2026. COLM 2026: The Third Annual Conference on Language Modeling. Open Review:
2025
- Liang, Y. 2025. Hubness awareness sampling for deep generative models in generation and evaluation. PhD Thesis , Cardiff University.
2024
- Liang, Y. et al. 2024. Deep generative model based rate-distortion for image downscaling assessment. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Seattle, WA, USA 17-21 June 2024. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE. , pp.19363-19372. (10.1109/CVPR52733.2024.01832)
- Liang, Y. et al. 2024. Efficient precision and recall metrics for assessing generative models using hubness-aware sampling. Presented at: The Forty-first International Conference on Machine Learning (ICML) Vienna, Austria 21-27 July 2024. Vol. 235., pp.29682-29699.
2023
- Song, S. et al. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023 Paris, France October 1 - 6, 2023. Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE. , pp.2360-2370. (10.1109/ICCV51070.2023.00224)
2022
- Liang, Y. et al. 2022. Exploring and exploiting hubness priors for high-quality GAN latent sampling. Presented at: The 39th International Conference on Machine Learning (ICML 2022) Baltimore, Maryland USA 17-23 July 2022. Vol. 162.ML Research Press. , pp.13271-13284.
Conferences
- Wang, Y. et al., 2026. GRADE: probing knowledge gaps in LLMs through gradient subspace dynamics. Presented at: COLM 2026: The Third Annual Conference on Language Modeling San Francisco, California 6 - 9 October 2026. COLM 2026: The Third Annual Conference on Language Modeling. Open Review:
- Liang, Y. et al. 2024. Deep generative model based rate-distortion for image downscaling assessment. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Seattle, WA, USA 17-21 June 2024. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE. , pp.19363-19372. (10.1109/CVPR52733.2024.01832)
- Liang, Y. et al. 2024. Efficient precision and recall metrics for assessing generative models using hubness-aware sampling. Presented at: The Forty-first International Conference on Machine Learning (ICML) Vienna, Austria 21-27 July 2024. Vol. 235., pp.29682-29699.
- Song, S. et al. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023 Paris, France October 1 - 6, 2023. Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE. , pp.2360-2370. (10.1109/ICCV51070.2023.00224)
- Liang, Y. et al. 2022. Exploring and exploiting hubness priors for high-quality GAN latent sampling. Presented at: The 39th International Conference on Machine Learning (ICML 2022) Baltimore, Maryland USA 17-23 July 2022. Vol. 162.ML Research Press. , pp.13271-13284.
Thesis
- Liang, Y. 2025. Hubness awareness sampling for deep generative models in generation and evaluation. PhD Thesis , Cardiff University.
Research
My research interests are centred around machine learning and its applications in computer vision, computer graphics and content generation. My PhD research, which is fully funded by the EPSRC, focuses on the hubness phenomenon to uncover the relationship between the hyper-dimensional distribution and the generative models.
Now my research is on explainability of generative models to make model-generated results more aligned with objective facts is a core challenge in AI, often referred to as AI alignment. The goal is to ensure that an AI system's behaviour and decisions align with human intent, values, and objective reality.
Contact Details
Research themes
Specialisms
- AI & Machine Learning
- Adversarial machine learning
- Computer vision
- Generative AI and LLMs