Shuang Wu

Shuang Wu

Ph.D. in Statistics

University of California, Los Angeles

About me

I am a Ph.D. candidate in Statistics at University of California, Los Angeles after spending two years on graduate studies at Purdue University. I am fortunate to be advised by Prof.Arash A. Amini and extremely happy to collaborate with Prof.Pan Li. My other collaborators include Yuantong Li, Chi-Hua Wang and Mingxuan Zhang etc. Before becoming a doctoral student, I received a BS in statistics from Sun Yat-sen University in 2017 and a MS in Biostatistics from Columbia University in 2019. My research interests are Machine Learning on Graphs and Bandit Algorithms.

Interests
  • Machine Learning on Graphs
  • Bandit Algorithms
Education

  • PhD in Statistics

    University of California, Los Angeles

  • MS in Biostatistics, 2019

    Columbia University

  • BS in Statistics, 2017

    Sun Yat-sen University

Projects

Last updated on Aug 18, 2022
Graph Federated Learning
Graph Federated Learning

Learning on Graphs (LoG) is widely used in multi-client systems when each client has insufficient local data, and multiple clients have to share their raw data to learn a model of good quality.

Last updated on Aug 18, 2022
Exploration in Bandit Algorithms
Exploration in Bandit Algorithms

We propose a new bootstrap-based online algorithm for stochastic linear bandit problems. The key idea is to adopt residual bootstrap exploration, in which the agent estimates the next step reward by re-sampling the residuals of mean reward estimate.

Papers

Shuang Wu, Arash A. Amini (2024). Graph Neural Thompson Sampling. RLC2024.

PDF Project

Shuang Wu, Mingxuan Zhang, Yuantong Li, Carl Yang, Pan Li (2022). Graph Federated Learning with Hidden Representation Sharing. Preprint.

PDF Project

Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng (2022). Residual Bootstrap Exploration for Stochastic Linear Bandit. UAI2022.

PDF Project