Guanghui Wang

PhD Student in Machine Learning
School of Computer Science
Georgia Institute of Technology
Office: CODA 12th Floor S1249J

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About Me

Hello! I am Guanghui Wang (王广辉), a third-year PhD student in Machine Learning at Georgia Institute of Technology. I am very fortunate to be advised by Prof. Jake Abernethy and Prof. Vidya Muthukumar.

Before joining Georgia Tech, I obtained my M.S. degree from Department of Computer Science and Technology in Nanjing University in 2020, where I was very fortunate to be advised by Prof. Lijun Zhang. I was also a member of the LAMDA group, led by Prof. Zhi-Hua Zhou. I received my B.E. degree from School of Electronic Engineering in Xidian University in 2017.

I am interested in online learning, stochastic optimization, and game theory.

Publications

Preprints

Conference Papers

  1. Faster Margin Maximization Rates for Generic Optimization Methods
    Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob Abernethy.
    NeurIPS 2023 (Spotlight).
  2. On Riemannian Projection-free Online Learning
    Zihao Hu, Guanghui Wang, Jacob Abernethy.
    NeurIPS 2023.
  3. Minimizing Dynamic Regret on Geodesic Metric Spaces
    Zihao Hu, Guanghui Wang, Jacob Abernethy.
    COLT2023. To appear.
  4. On Accelerated Perceptrons and Beyond
    Guanghui Wang, Rafael Hanashiro, Etash Guha, Jacob Abernethy.
    ICLR 2023.
  5. Adaptive Oracle-Efficient Online Learning
    Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob Abernethy.
    NeurIPS 2022.
  6. A Simple yet Universal Strategy for Online Convex Optimization
    Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang.
    ICML 2022.
  7. Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
    Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang.
    AISTATS 2022.
  8. Online Convex Optimization with Continuous Switching Constraint
    Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
    NeurIPS 2021.
  9. Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
    Lijun Zhang, Guanghui Wang, Wei-Wei Tu, Wei Jiang, Zhi-Hua Zhou.
    NeurIPS 2021.
  10. Stochastic Graphical Bandits with Adversarial Corruptions
    Shiyin Lu, Guanghui Wang, Lijun Zhang.
    AAAI 2021.
  11. Sadam: A Variant of Adam for Strongly Convex Functions
    Guanghui Wang, Shiyin Lu, Quan Cheng, Wei-Wei Tu, Lijun Zhang.
    ICLR 2020.
  12. Bandit Convex Optimization in Non-stationary Environments.
    Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou.
    AISTATS 2020.
  13. Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization
    Guanghui Wang, Shiyin Lu, Yao Hu, Lijun Zhang.
    AAAI 2020.
  14. Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs
    Bo Xue, Guanghui Wang, Yimu Wang, Lijun Zhang.
    IJCAI 2020.
  15. Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization
    Guanghui Wang, Shiyin Lu, Lijun Zhang.
    UAI 2019.
  16. Multi-Objective Generalized Linear Bandits
    Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang.
    IJCAI 2019.
  17. Optimal Algorithms for Lipschitz Bandits with Heavy-Tailed Rewards
    Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang.
    ICML 2019.
  18. Minimizing Adaptive Regret with One Gradient per Iteration
    Guanghui Wang, Dakuan Zhao, Lijun Zhang.
    IJCAI 2018.

Journal Articles

  1. Projection-free Distributed Online Learning with Sublinear Communication Complexity
    Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang.
    JMLR 2022.
  2. Bandit Convex Optimization in Non-stationary Environments
    Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou.
    JMLR 2021.

Academic Service

Reviewer: ICML, ICLR, NeurIPS, AISTATS, TMLR

Teaching

  1. TA, ECE 8803 Online Decision Making in Machine Learning, Fall 2021
  2. TA, co-instructor (6 lectures), ECE 8803 Online Decision Making in Machine Learning, Fall 2022
  3. TA, co-instructor (6 lectures), CS7545 Machine Learning Theory, Spring 2023
  4. co-instructor (2 lectures), ECE 8803 Online Decision Making in Machine Learning, Fall 2023

Working\Visiting Experience

  1. Visiting Graduate Student, Spring 2021, Simons Institute for the Theory of Computing.
  2. Reseach Assistant, Fall 2020 - Fall 2021, Nanjing University.

Awards

  1. ARC-ACO Fellowship, 2022.
  2. National Scholarship, 2014, 2018.