arXiv (OAI Expanded)
open access
Tight Heavy-Tailed Complexity Bounds for Optimizing Polyak--Łojasiewicz Objectives
We study optimizing $L$-smooth and $μ$-Polyak-Lojasiewicz (PL) objectives with unbiased stochastic gradients under $α$-heavy-tailed noise ($1<α\le 2$). In the unrestricted high-dimensional setting, we establish the noise-adaptive lower bound for attaining $ε$-suboptimal function value. Under the gen
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
- Revisiting AdaGrad in Stochastic Convex Optimization: Last Iterates, High Probability, and Lower Bounds#1117060
- Nonminimizing Attractors of Stochastic Subgradient Methods on Semialgebraic Functions#1248578
- New Bounds for the Last Iterate of the Stochastic subGradient Method#303191
- Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization#1051471
- Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe#1298075
Record · ID 1257981
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