Research map / Bandits and online learning / Multi-armed bandits
On the Finite-Time Performance of the Knowledge Gradient Algorithm
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the gap between the best and other arms | KG sampling-rate bounds · Instance 3 vs. Instance 1 | sampling-rate bound tightness | Helped |
| Increase the reward variances | KG sampling-rate bounds · Instance 4 vs. Instance 1 | sampling-rate bound tightness | Hurt |
| Increase the number of arms | KG sampling-rate bounds · Instance 5 vs. Instance 1 | sampling-rate bound tightness | Helped |
| Use KG for fixed-budget best-arm identification | Knowledge Gradient · BAI under independent normal rewards with known variances | probability of error and simple regret | Helped |
| Use KG in the cumulative-regret setting | Knowledge Gradient · MAB under independent normal rewards with known variances | cumulative regret | Hurt |
Closest papers by meaning
- Optimal Multi-Fidelity Best-Arm Identification (NeurIPS 2024 · similarity 0.88)
- Optimal Batched Best Arm Identification (NeurIPS 2024 · similarity 0.88)
- Balancing Performance and Costs in Best Arm Identification (NeurIPS 2025 · similarity 0.88)
- Fixed Confidence Best Arm Identification in the Bayesian Setting (NeurIPS 2024 · similarity 0.87)
- Near Optimal Best Arm Identification for Clustered Bandits (ICML 2025 · similarity 0.87)
- Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.