General
On the Rate of Gaussian Approximation for Online Linear Regression Problems
arXiv stat.ML0 views
arXiv:2509.14039v2 Announce Type: replace Abstract: In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We derive the corresponding rates for the setting of a constant stepsize and study the explicit dependence of the convergence rate on the problem dimension $d$ and quantities related to the design matrix. When the number of iterations $n$ is known in advance, our results yield a normal approximation rate of order $\sqrt{\log{n}/n}$, provided that the sample size $n$ is large enough.
Source:https://arxiv.org/abs/2509.14039PreviousSpectral and Subspace Dynamics of LoRA in Supervised Fine-Tuning and Reinforcement LearningNextVariational Estimators for Node Popularity Models
Related posts
The American Dream Wasn’t Built for Climate Change
0Wired
Bong: DuckDuckGo bang alternative, open-source and self-hosted
0Hacker News
Meridian — an experimental web browser built on Servo and GPUI
0Lobsters
Every processor die anyone has photographed
0Hacker News New
60 years of denial about Israel's nuclear weapons program confronts new reality
0Hacker News New
Missile Thermal Batteries
0Hacker News New