AI RESEARCH
Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression
arXiv CS.LG
•
ArXi:2503.03426v2 Announce Type: replace We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where the task is to minimize the in-sample mean squared error. Our main result shows that some of the sharpest risk bounds for the least squares estimator (LSE), based on the local Gaussian width, extend to ESMD. We derive sufficient conditions on the potential, expressed via the Minkowski functional, under which our result holds.