arXiv (All)
open access
Online Covariance Estimation for Averaged Stochastic Gradient Descent on Riemannian Manifolds
We study online inference for averaged Riemannian stochastic gradient descent (RSGD) when the unknown parameter lies on a manifold. Although the averaged RSGD iterate is asymptotically normal, practical inference requires estimating the unknown covariance operator in its limiting distribution. Exist
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- High order integration of stochastic dynamics on Riemannian manifolds with frozen flow methods#1296951
- Flatland: The Adventures of Gradient Descent with Large Step Sizes#1347119
- Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent#1345267
- Autoregressive Processes on Riemannian Manifolds#670650
- Autoregressive Processes on Riemannian Manifolds#668585
Record · ID 1455900
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive