I am a fourth-year PhD student from National University of Singapore, advised by Assoc. Prof. Bryan Low and Prof. See-Kiong Ng. Before that, I was a full time machine learning engineer at Ant Group. I received my Bachelor's degree from the Fudan University, where I worked with Assoc. Prof. Zhongyu Wei.
Recent Updates
[26 Sep 2024] Our 3 papers are accepted by NeurIPS 2024!
[20 Sep 2024] Our position paper is accepted by EMNLP Findings 2024!
[5 Aug 2024] I receive Research Achievement Award from NUS School of Computing.
[8 Jun 2024] Our 3 papers on prompt optimization and data attribution for LLMs are avaliable on arxiv now.
[2 May 2024] Our 3 papers are accepted by ICML 2024!
[5 Oct 2023] Our instruction optimization paper (INSTINCT) is avaliable on arxiv now.
[10 Aug 2023] I receive Research Achievement Award from NUS School of Computing.
[8 Aug 2023] Our paper "Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients" is avaliable on arxiv now.
[24 Apr 2023] Our paper "Fair yet Asymptotically Equal Collaborative Learning" is accepted by ICML 2023.
Research Interests
Data-centric AI for large models (e.g., data selection/curation in different stages of training for large models and use of data at inference).
Collaborative machine learning (e.g., federated learning, incentive mechanism).
Data valuation (e.g., data pricing, data subset selection, data debugging).
Gaussian process and its applications (e.g., zeroth-order optimization, Bayesian optimization).
Publications
Zhaoxuan Wu*, Xiaoqiang Lin*, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low.
Neural Information Processing Systems (NeurIPS), 2024.
Also presented at ICML 2024, Workshop on In-Context Learning
Paper / Code
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low.
Neural Information Processing Systems (NeurIPS), 2024.
Also presented at ICML 2024, Workshop on In-Context Learning
Paper / Code
Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low.
Neural Information Processing Systems (NeurIPS), 2024.
Also presented at ICML 2024, Workshop on In-Context Learning
Paper
Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low.
Empirical Methods in Natural Language Processing Findings (EMNLP Findings), 2024.
Paper
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low.
International Conference on Machine Learning (ICML), 2024.
Paper
Xiaoqiang Lin*, Zhaoxuan Wu*, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low.
International Conference on Machine Learning (ICML), 2024.
Also presented at NeurIPS 2023, Workshop on Instruction Tuning and Instruction Following
Project Page / Paper / Code / Bibtex
Jingtan Wang*, Xiaoqiang Lin*, Rui Qiao*, Chuan-Sheng Foo, Bryan Kian Hsiang Low.
International Conference on Machine Learning (ICML), 2024.
Paper / Code
Xiaoqiang Lin*, Xinyi Xu*, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low.
International Conference on Machine Learning (ICML), 2023.
Paper / Code / Bibtex
Yuqiao Yang*, Xiaoqiang Lin*, Geng Lin, Zengfeng Huang, Changjian Jiang, Zhongyu Wei.
International Joint Conferences on Artificial Intelligence (IJCAI), 2020.
Paper / Code / Bibtex
Book Chapters
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, Rachael Hwee Ling Sim, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang,, 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.
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, 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.
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, 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.
Workshop Papers & Pre-Prints
Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low.
ICML 2024, Workshop on Foundations of RL and Control.
Paper
Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low.
ICML 2024, Workshop on Models of Human Feedback for AI Alignment.
Selected as Oral
Oral presentation / Paper / Code
Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low.
Pre-print, 2023. [arXiv]
Paper / Bibtex
Invited Talks
@ Deep Learning and Optimization Seminar (Jointly organized by Westlake University, City University of Hong Kong, Peking University). Oct 24, 2023. Video
Awards and Honors
Award of Teaching Fellowship, National University of Singapore, 2023 (1 of 3 selected CS Ph.D. students).Research Achievement Award × 2, NUS, School of Computing, 2023 & 2024.
Outstanding Graduates, Fudan University, 2020.
Excellent Student Scholarship, Fudan University, 2017, 2018, 2019.
Professional Services
Conference Reviewer for ICLR’2024.Conference Reviewer for NeurIPS’2024.
Conference Reviewer for AISTATS’2024.
Conference Reviewer for AAAI’2023, 2024.
Conference Reviewer for ACML’2023.
Conference Reviewer for ICML’2021, 2024.
Contact
You are very welcome to contact me regarding my research. I am open to any talk invitations on my research. I typically respond within a few days.I can be contacted directly at xiaoqiang.lin [at] u.nus.edu