Tutorials, Short Courses and Lecture Series
Statistical and Algorithmic Foundations of Reinforcement Learning
JSM 2023, together with Yuting Wei and Yuejie Chi
Nonasymptotic Analysis for Reinforcement Learning
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 highdimensional 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
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
