Yuantong Li

PhD Student Department of Statistics University of California, Los Angeles (UCLA) Office: BH 9410 E-mail: yuantongli [@] ucla [DOT] edu Address: Boelter Hall, Los Angeles, CA 90095 Google Scholar

About me

I’m a 4th-year Ph.D. student in the Department of Statistics, UCLA. My advisor is Prof. Guang Cheng and Prof. Xiaowu Dai. I was also fortunate to be advised by Prof. Wei Sun in Purdue University. My research interests fall in high-dimensional statistics, trustworthy AI combined with sequential decision making (bandit theory and reinforcement learning), and designing and optimizing multi-agent systems. Besides, I have interests and experience in natural language process field. CV is available upon request.

Some of our current and past research interests are

• Sequential decision making

• Mechanism design

• Trustworthy AI (Fairness and Causal Inference)

• High-dimensional statistics

• NLP

News

• Nov 2022, I'm going to attend the Societal Considerations and Applications in Simons Institute for the Theory of Computing and to present our paper Rate-Optimal Contextual Online Matching Bandit.

• Oct 2022, I will attend the Informs Annual Meeting (Indianapolis, IN) to present our paper Rate-Optimal Contextual Online Matching Bandit!

• July 2022, I will attend the NACCL 2022 (Seattle, WA) to present our paper!

• June 2022, our Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning paper got accepted at JASA! Congratulations to all my collaborators!

Education

• Ph.D. in Statistics, UCLA, 2022 - Present.

• Ph.D. in Statistics, Purdue University, 2019 - 2021.

• M.S. in Statistics, North Carolina State University, 2016 - 2018.

• B.S. in Mathematics, Shing-Tung Yau’s Mathematics Elite Class, Chu Kochen Honor College, Zhejiang University, 2012 - 2016.

Publications/Manuscripts

• Double Matching Under Complementary Preferences
  Y. Li, G. Cheng, X. Dai.
  

• Rate-Optimal Contextual Online Matching Bandit
  Y. Li, C. Wang, G. Cheng, W. Sun.
  

• Graph Federated Learning with Hidden Representation Sharing
  S. Wu, M. Zhang, Y. Li, C. Yang, P. Li.
  

• Residual Bootstrap Exploration for Stochastic Linear Bandit
  S. Wu, C. Wang, Y. Li, G. Cheng.
  Proceedings of the 38th International Conference on Uncertainty in Artificial Intelligence, (UAI 2022).
  

• Debiasing Neural Retrieval via In-batch Balancing Regularization
  Y. Li, X. Wei, Z. Wang, S. Wang, P. Bhatia, X. Ma, and A. Arnold.
  Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing, (GeBNLP 2022).
  

• Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
  P. Ramprasad, Y. Li, Z. Yang, Z. Wang, W. Sun., and G. Cheng.
  Journal of the American Statistical Association, (JASA).
  

• Peel learning for pathway-related outcome prediction
  Y. Li, F. Wang, M. Yang, F. Yang, E. Cantu, H. Rao, and R. Feng.
  Bioinformatics.
  

• Online Forgetting Process for Linear Regression Models
  Y. Li, C. Wang, and G. Cheng.
  Proceedings of the 24nd International Conference on Artificial Intelligence and Statistics, (AISTATS 2021).
  

• A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components
  Y. Li, Q. Ma, and S. K. Ghosh.
  The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020, (KDD 2020)
  

• Interactive Attention Networks for Semantic Text Matching
  S. Zhao, Y. Huang, C. Su, Y. Li and F. Wang.
  2020 IEEE International Conference on Data Mining, (ICDM 2020)
  

Work Experiences

• Applied Scientist Intern, Amazon Web Service (AWS) AI Lab, June 2022 - Sep 2022 (Intern).
  

• Applied Scientist Intern, Amazon Web Service (AWS) AI Lab, May 2021 - Aug 2021 (Intern).
  

Professional Services

• Conference reviewer/PC member: AISTATS, SDM, KDD, Neurips, ICML, IJCAI, UAI.

• Journal Reviewer: IEEE TNNLS, STAT.