Tutorials, Short Courses and Lecture Series

• Spectral Methods for Data Science: A Statistical Perspective
  

  • 2023 Summer School on Theoretical Statistics, PKU BICMR & Math

• Statistical and Algorithmic Foundations of Reinforcement Learning
  

  • JSM 2023, together with Yuting Wei and Yuejie Chi

• Non-asymptotic Analysis for Reinforcement Learning [Part 1][Part 2][Part 3]
  

  • SIGMETRICS 2023, together with Yuting Wei and Yuejie Chi

• Reinforcement Learning: Fundamentals, Algorithms, and Theory
  

  • ICASSP 2022, together with Yuting Wei and Yuejie Chi

• Statistical and algorithmic foundations of reinforcement learning
  

  • ICSA Applied Statistics Symposium 2021, together with Yuting Wei, Yuejie Chi and Zhengyuan Zhou

• Taming nonconvexity in information science
  

  • ITW 2018, together with Yuejie Chi

• Recent advances in nonconvex methods for high-dimensional estimation
  

  • ICASSP 2018, together with Yuejie Chi and Yue Lu

• TRIAD Lecture Series 2019, Georgia Tech

  • The power of nonconvex optimization in solving random quadratic systems of equations

  • Random initialization and implicit regularization in nonconvex statistical estimation

  • Projected power method: an efficient nonconvex algorithm for joint discrete assignment

  • Spectral methods meets asymmetry: two recent stories

  • Inference and uncertainty quantification for noisy matrix completion

A few invited Talks

1. Inference and uncertainty quantification for low-rank models
   

2. Demystifying the efficiency of reinforcement learning: a few recent stories
   

3. On the effectiveness of nonconvex optimization in reinforcement learning
   

4. Taming nonconvexity in tensor completion: Fast convergence and uncertainty quantification
   

5. Taming nonconvexity in statistical and reinforcement learning
   

6. Breaking the sample size barrier in reinforcement learning via model-based approaches (a.k.a. plug-in approaches)
   

7. Nonconvex optimization meets statistics: a few recent stories
   

8. Inference and uncertainty quantification for noisy matrix completion
   

9. Bridging convex and nonconvex optimization in noisy matrix completion: stability and uncertainty quantification
   

10. Stability, nonconvex optimization, and asymmetry in low-rank matrix estimation
    

11. Noisy matrix completion: understanding statistical guarantees of convex relaxation via nonconvex optimization
    

12. Random initialization and implicit regularization in nonconvex statistical estimation
    

13. Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices
    

14. Implicit regularization in nonconvex statistical estimation
    

15. Spectral method and regularized MLE are both optimal for top-K ranking
    

16. The projected power method: a nonconvex algorithm for discrete problems
    

17. The projected power method: an efficient algorithm for joint alignment from pairwise differences
    

18. Solving random quadratic systems of equations is nearly as easy as solving linear systems
    

19. Modern optimization meets physics: recent progress on phase retrieval
    

20. Near-optimal joint object matching via convex relaxation