Publications

Design for Triangular Rational Bezier Harmonic and Biharmonic Surfaces (in Chinese) [pdf]
Shuai Li, Xiaoqian Xu, Guojin Wang
Journal of Zhejiang University (Science Edition) 2012 39 (2).

• Online Influence Maximization under Linear Threshold Model [arXiv]
  Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen.
  Accepted in NeurIPS, 2020.

• A Survey on Online Influence Maximization (in Chinese) [link]
  Fang Kong, Qizhi Li, Shuai Li.
  Computer Science, 2020.

• The Gambler’s Problem and Beyond [arXiv]
  Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan.
  Eighth International Conference on Learning Representations (ICLR), 2020.
  Also presented at the OptRL workshop in NeurIPS 2019.

• Stochastic Online Learning with Probabilistic Graph Feedback [arXiv][link][slides][poster]
  Shuai Li, Wei Chen, Zheng Wen, Kwong-Sak Leung.
  The 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.

• Predicting Associations among Drugs, Targets and Diseases by Tensor Decomposition for Drug Repositioning [link]
  Ran Wang, Shuai Li, Lixin Cheng, Man-Hon Wong, Kwong-Sak Leung.
  BMC Bioinformatics, 2019.
  Previous conference version:
  Drug-Protein-Disease Association Prediction and Drug Repositioning Based on Tensor Decomposition [link]
  Ran Wang, Shuai Li, Man-Hon Wong, Kwong-Sak Leung.
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018.

• Improving Prediction of Phenotypic Drug Response on Cancer Cell Lines Using Deep Convolutional Network [link]
  Pengfei Liu, Hongjian Li, Shuai Li, Kwong-Sak Leung.
  BMC Bioinformatics, 2019.

• Improved Algorithm on Online Clustering of Bandits [link][arXiv][code][slides]
  Shuai Li, Wei Chen, S Li, Kwong-Sak Leung.
  The 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

• Online Learning to Rank with Features [link][arXiv][code][slides][poster]
  Shuai Li, Tor Lattimore, Csaba Szepesvari.
  The 36th International Conference on Machine Learning (ICML), 2019.

• TopRank: A Practical Algorithm for Online Stochastic Ranking [link][arXiv][poster]
  Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvari.
  The 32nd Conference on Neural Information Processing Systems (NeurIPS). 2018.

• Offline Evaluations of Ranking Policies with Click Models [link][arXiv][slides][poster][video]
  Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, Zheng Wen.
  The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD Research Track). 2018.
  Also presented in the CausalML Workshop of ICML 2018.

• Contextual Dependent Click Bandit Algorithm for Web Recommendation [pdf]
  Weiwen Liu, Shuai Li, Shengyu Zhang.
  International Computing and Combinatorics Conference (COCOON), pp. 39-50. Springer, Cham, 2018.

• Online Clustering of Contextual Cascading Bandits [link][arXiv][slides][poster]
  Shuai Li, Shengyu Zhang.
  The 32nd AAAI Conference on Artificial Intelligence (AAAI). 2018.

• Contextual Combinatorial Cascading Bandits [link][slides][poster]
  Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen.
  The 33rd International Conference on Machine Learning (ICML), pp. 1245-1253. 2016.
  Also presented in the International Doctoral Forum (oral), Hong Kong, 2016.

• A Hybrid Distributed Framework for SNP Selections [pdf]
  Pengfei Liu, Shuai Li, Weiying Yi, Kwong-Sak Leung.
  Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), p. 192.
  The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016.

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Shuai Li I am a tenure-track assistant professor in John Hopcroft Center of Shanghai Jiao Tong University. I received my PhD degree in the Chinese University of Hong Kong under the supervision of Prof. Kwong-Sak Leung. Before that, I obtained my bachelor degree in Mathematics from Zhejiang University and my master degree in Mathematics from University of the Chinese Academy of Sciences. My research interest lies on multi-armed bandits, online learning, machine learning theory, reinforcement learning, and recommendation systems. I am looking forward to research collaborations with industry and academia. Please contact me if you are interested. Prospective students: I am always looking for outstanding and highly motivated students to work together on bandits, machine learning theory and recommendation systems. Please email me with your CV and transcripts if you are interested. Email: shuaili8@sjtu.edu.cn CV GoogleScholar ResearchGate LinkedIn GitHub