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Found 3 papers, 1 papers with code
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Considering two prototypical choices for the nonlinearity, we study the convergence properties of a natural alternating update rule for this nonconvex optimization problem starting from a random initialization.
Sharp global convergence guarantees for iterative nonconvex optimization: A Gaussian process perspective
In particular, provided each iteration can be written as the solution to a convex optimization problem satisfying some natural conditions, we leverage Gaussian comparison theorems to derive a deterministic sequence that provides sharp upper and lower bounds on the error of the algorithm with sample-splitting.
Imputation for High-Dimensional Linear Regression
We study high-dimensional regression with missing entries in the covariates.
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