Dr Ziquan Liu
Lecturer
School of Electronic Engineering and Computer Science
Queen Mary University of London
Queen Mary University of London
Research
Reliable Machine Learning, Uncertainty Quantification, Computer Vision
Interests
My research interest is machine learning, including trustworthy and robust machine learning, uncertainty of foundation models and interpretable machine learning. The main goal of my research is to quantify and mitigate the risk of machine learning systems so that the deployment of machine learning benefits each individual in our society.Publications
Publications of specific relevance to the Centre for Multimodal AI2025
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial AttacksFeng C, Liu Z, Zhi Z, Bogunovic I, Gerner-Beuerle C and Rodrigues M
Proceedings of the AAAI Conference on Artificial Intelligence. vol. 39 (3), 2933-2941.
11-04-2025
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning
Biased Models with Contextual Synthetic DataZhao Z, Liu Z, Cao Y, Gong S and Patras I
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Nashville, US 11 Jun 2025 - 16 Jun 2025.
07-03-2025
Wasserstein Modality Alignment Makes Your Multimodal Transformer More RobustZhi Z, Sun Y, Wu Q, Liu Z and Rodrigues M
Transactions on Machine Learning Research
23-01-2025
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural DimensionsMasoud RI, Liu Z, Ferianc M, Treleaven P and Rodrigues M
Proceedings - International Conference on Computational Linguistics, COLING. vol. Part F206484-1, 8474-8503.
01-01-2025
Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy MaximizationZhang Z, Liu Z and Patras I
Proceedings - International Conference on Computational Linguistics, COLING. vol. Part F206484-1, 10924-10939.
01-01-2025
2024
A Secure Image Watermarking Framework with Statistical Guarantees via Adversarial Attacks on Secret Key NetworksChen F, Lin W, Liu Z and Chan AB
Lecture Notes in Computer Science. vol. 15098, 428-445.
10-11-2024
Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy MaximizationZhang Z, Liu Z and Patras I
09-08-2024
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksLiu Z, Cui Y, Yan Y, Xu Y, Ji X, Liu X and Chan AB
Proceedings of Machine Learning Research. vol. 235, 30908-30928.
01-01-2024
2023
Variational Nested DropoutCui Y, Mao Y, Liu Z, Li Q, Chan AB, Liu X, Kuo T-W and Xue CJ
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE) vol. 45 (8), 10519-10534.
30-06-2023
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and GeneralizationLiu Z, Xu Y, Ji X and Chan AB
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). vol. 00, 16436-16446.
24-06-2023
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksWu Q, Yang T, Liu Z, Wu B, Shan Y and Chan AB
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). vol. 00, 14561-14571.
24-06-2023
Clustering Hidden Markov Models With Variational Bayesian Hierarchical EMLan H, Liu Z, Hsiao JH, Yu D and Chan AB
IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE) vol. 34 (3), 1537-1551.
28-02-2023
Retrieval-Augmented Multiple Instance LearningCui Y, Liu Z, Chen Y, Lu Y, Yu X, Liu X, Kuo TW, Rodrigues MRD, Xue CJ and Chan AB
Advances in Neural Information Processing Systems. vol. 36
01-01-2023
BAYES-MIL: A NEW PROBABILISTIC PERSPECTIVE ON ATTENTION-BASED MULTIPLE INSTANCE LEARNING FOR WHOLE SLIDE IMAGESCui Y, Liu Z, Liu X, Wang C, Kuo TW, Xue CJ and Chan AB
11th International Conference on Learning Representations, ICLR 2023.
01-01-2023
2022
PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture ModelsLiu Z, Yu L, Hsiao JH and Chan AB
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE) vol. 44 (6), 3197-3211.
05-05-2022
Improved Fine-Tuning by Better Leveraging Pre-Training DataLiu Z, Xu Y, Qian Q, Li H, Ji X, Chan AB and Jin R
Advances in Neural Information Processing Systems. vol. 35
01-01-2022
Boosting Adversarial Robustness From The Perspective of Effective Margin RegularizationLiu Z and Chan AB
BMVC 2022 - 33rd British Machine Vision Conference Proceedings.
01-01-2022
2021
Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive CompressionCui Y, Liu Z, Li Q, Chan AB and Xue CJ
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). vol. 00, 2392-2401.
25-06-2021
A Generalized Loss Function for Crowd Counting and LocalizationWan J, Liu Z and Chan AB
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). vol. 00, 1974-1983.
25-06-2021
2020
Fully nested neural network for adaptive compression and quantizationCui Y, Liu Z, Yao W, Li Q, Chan AB, Kuo TW and Xue CJ
IJCAI International Joint Conference on Artificial Intelligence. vol. 2021-January, 2080-2087.
01-01-2020
2019
Parametric Manifold Learning of Gaussian Mixture ModelsLiu Z, Yu L, Hsiao JH and Chan AB
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence., 3073-3079.
01-08-2019