Research output per year
Research output per year
Personal profile
Author IDs
ORCID iD: 0000-0002-9585-3991
Scopus Author ID: 57215725422
Google Scholar Profile: 48PsswEAAAAJ
Impact
Qualifications (Brief)
BEng(Tsinghua), MSc(EPFL), PhD(EPFL)
Biography
Chen Liu is an assistant professor in the department of computer science, City University of Hong Kong since September 2022. He obtained his Ph.D. degree from EPFL in August 2022. Previously, he obtained his Master degree from EPFL in 2017 and Bachelor degree from Tsinghua University in 2015, both in computer science.
Chen was supported by Microsoft Research Scholarship between from 2017 to 2019. His research interests focuses on machine learning and optimization, particularly the reliablity of deep neural networks.
Position(s) Available
Information about available positions is posted on this website.
Research Interests/Areas
Machine Learning; Optimization; Deep Neural Networks; Robustness
Teaching
GE2340: Artificial Intelligence: Past, Present and Future
CS1102: Introduction to Computer Studies
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Collaborations from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Research output
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Toward Mitigating Architecture Overfitting on Distilled Datasets
Zhong, X. & Liu, C., 15 Jan 2025, (Online published) In: IEEE Transactions on Neural Networks and Learning Systems.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density
Li, S., Liu, C., Zhang, T., Le, H., Süsstrunk, S. & Salzmann, M., Oct 2024, In: Transactions on Machine Learning Research.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Open Access -
Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning
Yang, X., Liu, C. & Wei, Y., 2024, NeurIPS Proceedings: Advances in Neural Information Processing Systems 37 (NeurIPS 2024). Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J. & Zhang, C. (eds.). Neural Information Processing Systems (NeurIPS), (Advances in Neural Information Processing Systems).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Open Access -
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
Liu, C., Huang, Z., Salzmann, M., Zhang, T. & Süsstrunk, S., 2024, In: Journal of Machine Learning Research. 25, 356.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Open AccessFile15 Downloads (CityUHK Scholars) -
Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations
Zhong, X., Huang, Y. & Liu, C., 2024, Proceedings of the 41st International Conference on Machine Learning. ML Research Press, p. 61708-61726 (Proceedings of Machine Learning Research; vol. 235).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Open Access
Projects
- 5 Active
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DON: Accelerated and Memory Efficient Fine-tuning of Large Language Models
LIU, C. (Principal Investigator / Project Coordinator)
1/06/25 → …
Project: Research
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DON: Towards Robust and Privacy-preserving Large Language Models - RMGS
LIU, C. (Principal Investigator / Project Coordinator)
1/05/24 → …
Project: Research
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DON: Towards Robust and Privacy-preserving Large Language Models
LIU, C. (Principal Investigator / Project Coordinator)
1/05/24 → …
Project: Research
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NSFC-YPC-HK: Efficient Adversarial Attacks and Adversarial Training Algorithms for Discrete Targets
LIU, C. (Principal Investigator / Project Coordinator)
1/01/24 → …
Project: Research
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DON_RMG: Towards More Efficient and Robust Object Detection Models against Adversarial Patches for Auto-driving - RMGS
LIU, C. (Principal Investigator / Project Coordinator)
1/06/23 → …
Project: Research