From Stochastic to Deterministic: SGD dynamics of non-convex models in high dimensionsStochastic gradient descent (SGD) stands as a cornerstone of optimization and modern machine learning. However, understanding why SGD performs so well remains a major challenge. In this talk, I will present a theory for SGD in high dimensions when the number of samples and […]

TBD

Tikhonov regularization is a widely used technique in solving inverse problems that can enforce prior properties on the desired solution. In this talk, I will present a Krylov subspace based iterative method for solving linear inverse problems with general-form Tikhonov regularization term $x^top M x$ , where $M$ is a positive semidefinite matrix. An iterative […]