I am a Ph.D. student in Data Science at the National University of Singapore (NUS), supervised by Assoc. Prof. Bryan Kian Hsiang Low. I received my Bachelor of Science (Honors) in Data Science & Analytics and a minor in Computer Science from NUS in 2020. I am currently supported by the President’s Graduate Fellowship jointly offered by the NUS Graduate School Integrative Sciences and Engineering Programme (ISEP) and the Institute of Data Science (IDS), and the Singapore Data Science Consortium (SDSC) Dissertation Research Fellowship.

Research Interests

My interests include, but not limited to, the following areas:

Collaborative machine learning (e.g., data valuation, federated learning, incentives, fairness)
Resource-efficient machine learning (e.g., Bayesian optimization)
Large language models (e.g., prompting)
Deep learning & applications

Publications

* = equal contribution

Incentive-Aware Federated Learning with Training-Time Model Rewards.
Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, and Bryan Kian Hsiang Low.
In Proceedings of the 12th International Conference on Learning Representations (ICLR-24).
Localized Zeroth-Order Prompt Optimization.
Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, and Bryan Kian Hsiang Low.
In arXiv Pre-print.

Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers.
Xiaoqiang Lin*, Zhaoxuan Wu*, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low.
In the 37th Conference on Neural Information Processing Systems (NeurIPS’23) Workshop on Instruction Tuning and Instruction Following.
[project page | code]
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery.
Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, and Bryan Kian Hsiang Low.
In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS-23).
[code]

DAVINZ: Data Valuation using Deep Neural Networks at Initialization.
Zhaoxuan Wu, Yao Shu, and Bryan Kian Hsiang Low.
In Proceedings of the 39th International Conference on Machine Learning (ICML-22).
[code]
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, and Bryan Kian Hsiang Low.
In Advances in Neural Information Processing Systems 35: 36th Annual Conference on Neural Information Processing Systems (NeurIPS’22).
[code]

Validation Free and Replication Robust Volume-based Data Valuation.
Xinyi Xu*, Zhaoxuan Wu*, Chuan Sheng Foo, and Bryan Kian Hsiang Low.
In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS’21).
[code]
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization.
Quoc Phong Nguyen*, Zhaoxuan Wu*, Bryan Kian Hsiang Low, and Patrick Jaillet.
In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21).
[code]

Data Valuation in Federated Learning.
Zhaoxuan Wu, Xinyi Xu, Rachael Hwee Ling Sim, Yao Shu, Xiaoqiang Lin, Lucas Agussurja, Zhongxiang Dai, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 15, pages 281-296, Academic Press, 2024.
Fairness in Federated Learning.
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, Rachael Hwee Ling Sim, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 8, pages 143-160, Academic Press, 2024.
Incentives in Federated Learning.
Rachael Hwee Ling Sim, Sebastian Shenghong Tay, Xinyi Xu, Yehong Zhang, Zhaoxuan Wu, Xiaoqiang Lin, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 16, pages 299-309, Academic Press, 2024.