Information, Inference and Machine Learning Group at University College London

Ziquan Liu

Postdoc Fellow

Contact

Where to Find Me

Malet Place Engineering Building
Torrington Place
London WC1E 7JE
United Kingdom

Research Interests

Trustworthy Machine Learning, Computer Vision

About

Ziquan Liu is a Postdoc Research Fellow at University College London, working with Prof. Miguel Rodrigues and Prof. Carsten Gerner-Beuerle. He obtained Bachelor of Engineering in Information Engineering from Beihang University and secondary Bachelor of Science in Mathematics from the same university, both in 2017. He spent five wonderful years at VISAL lab of City University of Hong Kong under the supervision of Prof. Antoni B. Chan and got his PhD degree in 2022.

Selected Publications

@inproceedings{yufeibayes,
                  title={Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images},
                  author={Yufei, Cui and Liu, Ziquan and Liu, Xiangyu and Liu, Xue and Wang, Cong and Kuo, Tei-Wei and Xue, Chun Jason and Chan, Antoni B},
                  booktitle={International Conference on Learning Representations (ICLR)},
                  year={2023},
                }
                @inproceedings{liu2021improved,
                  title={Improved Fine-Tuning by Better Leveraging Pre-Training Data},
                  author={Liu, Ziquan and Xu, Yi and Xu, Yuanhong and Qian, Qi and Li, Hao and Ji, Xiangyang and Chan, Antoni B and Jin, Rong},
                  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
                  year={2022},
                }
                @inproceedings{liu2022empirical,
                  title={An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation},
                  author={Liu, Ziquan and Xu, Yi and Xu, Yuanhong and Qian, Qi and Li, Hao and Jin, Rong and Ji, Xiangyang and Chan, Antoni B},
                  booktitle={NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications},
                  year={2022},
                }
                @inproceedings{Liu_2022_BMVC,
                  author    = {Ziquan Liu and Antoni Chan},
                  title     = {Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization},
                  booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
                  year      = {2022},
                  }
                @article{liu2021primal,
                  title={PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models},
                  author={Liu, Ziquan and Yu, Lei and Hsiao, Janet H and Chan, Antoni B},
                  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
                  volume={44},
                  number={6},
                  pages={3197--3211},
                  year={2021},
                  publisher={IEEE}
                }
                @inproceedings{liu2019parametric,
                  title={Parametric Manifold Learning of Gaussian Mixture Models.},
                  author={Liu, Ziquan and Yu, Lei and Hsiao, Janet H and Chan, Antoni B},
                  booktitle={IJCAI},
                  pages={3073--3079},
                  year={2019},
                }

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Ziquan Liu

Postdoc Fellow

Contact

Where to Find Me

Research Interests

About

Selected Publications

Refereed Articles

Books

Conference and Workshop Papers

Pre-prints and Other Publications