Tutorials, Short Courses and Lecture Series
A few invited Talks
Inference and uncertainty quantification for low-rank 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 model-based approaches (a.k.a. plug-in 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 low-rank 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 low-rank matrices
Implicit regularization in nonconvex statistical estimation
Spectral method and regularized MLE are both optimal for top-K 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
Near-optimal joint object matching via convex relaxation
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