Research map / Bandits and online learning / Multi-armed bandits
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
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 |
|---|---|---|---|
| Use MaxGapUCB or MaxGapTop2UCB instead of random sampling | MaxGap-bandit clustering · Streetview dataset | samples to achieve the same error: 6-7x fewer samples | Helped |
| Use adaptive sampling instead of non-adaptive sampling | MaxGap-bandit clustering · Simulated data, two large gaps | samples to identify the largest gap: 8x fewer samples | Helped |
Closest papers by meaning
- Identifying Outlier Arms in Multi-Armed Bandit (NeurIPS 2017 · similarity 0.89)
- Optimal Best-arm Identification in Linear Bandits (NeurIPS 2020 · similarity 0.89)
- The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits (NeurIPS 2026 · similarity 0.88)
- New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem (AAAI 2024 · similarity 0.88)
- Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids (ICLR 2025 · similarity 0.87)
- Eliciting Kemeny Rankings (AAAI 2024 · similarity 0.87)
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.