Tutorials, Short Courses and Lecture Series

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