arXiv (All)
open access
Strongly Adaptive Online Learning Achieves Uniform Discounted Regret
Discounted regret measures the performance of an online learner by assigning exponentially decaying weights to past losses, reflecting the greater importance of recent losses in non-stationary online learning. While previous work obtains uniform discounted regret bounds over a continuous range of di
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
- Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport#1201857
- Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Action Sets#1483183
- Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets#1343025
- Sharp Characterization of Bias in Post-Bandit Inference#1052257
- Lower Bounds for Linear-Oracle Online Learning#1142976
Record · ID 1490687
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