arXiv (All)
open access
Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivit
We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers. For $K$-armed bandits with $A$ optimal arms, we first provide a sharper analysis of previous sub-sampling algorithms (De Heide et al., 202
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
- Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates#665788
- Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates#664288
- Rising Multi-Armed Bandits with Known Horizons#1142620
- Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret#998552
- Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits#19029
Record · ID 1143260
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