Tutorials, Short Courses and Lecture Series
A few invited Talks
Inference and uncertainty quantification for lowrank models
Demystifying the efficiency of reinforcement learning: a few recent stories
On the effectiveness of nonconvex optimization in reinforcement learning
Taming nonconvexity in tensor completion: Fast convergence and uncertainty quantification
Taming nonconvexity in statistical and reinforcement learning
Breaking the sample size barrier in reinforcement learning via modelbased approaches (a.k.a. plugin approaches)
Nonconvex optimization meets statistics: a few recent stories
Inference and uncertainty quantification for noisy matrix completion
Bridging convex and nonconvex optimization in noisy matrix completion: stability and uncertainty quantification
Stability, nonconvex optimization, and asymmetry in lowrank matrix estimation
Noisy matrix completion: understanding statistical guarantees of convex relaxation via nonconvex optimization
Random initialization and implicit regularization in nonconvex statistical estimation
Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed lowrank matrices
Implicit regularization in nonconvex statistical estimation
Spectral method and regularized MLE are both optimal for topK ranking
The projected power method: a nonconvex algorithm for discrete problems
The projected power method: an efficient algorithm for joint alignment from pairwise differences
Solving random quadratic systems of equations is nearly as easy as solving linear systems
Modern optimization meets physics: recent progress on phase retrieval
Nearoptimal joint object matching via convex relaxation
