Professor LI, Guodong
Associate Head (Research) & Professor
Department of Statistics & Actuarial Science
The University of Hong Kong
Department of Statistics & Actuarial Science
The University of Hong Kong
gdli@hku.hk
(852) 3917 1986
(852) 3917 1986
About Me
Professor Guodong Li joined the Department of Statistics & Actuarial Science, The University of Hong Kong, in 2009 as an Assistant Professor, and currently is a Professor. Prior to this, Professor Li had worked at the Division of Mathematical Sciences, Nanyang Technological University, Singapore, as an Assistant Professor since he received his PhD degree in statistics from the University of Hong Kong in 2007. He got his Bachelor and Master degrees in Statistics from Peking University.
Research Interests
Time Series Analysis; Financial Econometrics; Quantile Regression; High-Dimensional Data Analysis; Machine Learning
Selected Publications
- F Huang, K Lu, Y Cai, Z Qin, Y Fang, G Tian & G Li (2023) Encoding Recurrence into Transformers, Proceedings of the 11th International Conference on Learning Representations (ICLR- 23). (The acceptance rate is 31.8%, and this is an oral paper, i.e. notabletop-5%)
- Y Fang, Y Cai, J Chen, J Zhao, G Tian & G Li (2023) Cross-Layer Retrospective Retrieving via Layer Attention, Proceedings of the 11th International Conference on Learning Representations (ICLR-23). (The acceptance rate is 31.8%)
- J Zhao, Y Fang & G Li (2021), Recurrence along depth: deep convolutional neural networks with recurrent layer aggregation, Advances in Neural Information Processing Systems (NeurIPS 2021). Vol. 34, pp.10627-10640. (GitHub, the acceptance rate is 26%.)
- J Zhao, F Huang, J Lv, Y Duan, Z Qin, G Li & G Tian (2020), Do RNN and LSTM have long memory? Proceedings of the 37th International Conference on Machine Learning (ICML-20). Vol. 119, pp.11365-11375. (The acceptance rate is 21.8%.)
- D Wang, F Huang, J Zhao, G Li & G Tian (2020), Compact autoregressive network, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). pp.6145-6152. (The acceptance rate is 20.6%)
- Q Zhu, S Tan, Y Zheng & G Li (2023), Quantile autoregressive conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B, to appear.
- D Wang, Y Zheng, H Lian & G Li (2022), High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association 117, 1338-1356.
- C Dong, G Li, & X Feng (2019), Lack-of-fit tests for quantile regression models, Journal of the Royal Statistical Society, Series B 81, 629-648.
- Y Zheng, Q Zhu, G Li & Z Xiao (2018), Hybrid quantile regression estimation for time series models with conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B 80, 975-993.
- G Li, Y Li, & C-L Tsai (2015), Quantile correlations and quantile autoregressive modeling, Journal of the American Statistical Association 110, 246-261.
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