Tutorials, Short Courses and Lecture Series

1. Statistical and Algorithmic Foundations of Reinforcement Learning
   

   1. JSM 2023, together with Yuting Wei and Yuejie Chi

2. Non-asymptotic Analysis for Reinforcement Learning
   

   1. SIGMETRICS 2023, together with Yuting Wei and Yuejie Chi

3. Reinforcement Learning: Fundamentals, Algorithms, and Theory
   

   1. ICASSP 2022, together with Yuting Wei and Yuejie Chi

4. Statistical and algorithmic foundations of reinforcement learning
   

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

5. Taming nonconvexity in information science
   

   1. ITW 2018, together with Yuejie Chi

6. Recent advances in nonconvex methods for high-dimensional estimation
   

   1. ICASSP 2018, together with Yuejie Chi and Yue Lu

7. TRIAD Lecture Series 2019, Georgia Tech

   1. The power of nonconvex optimization in solving random quadratic systems of equations

   2. Random initialization and implicit regularization in nonconvex statistical estimation

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

   4. Spectral methods meets asymmetry: two recent stories

   5. 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