Research map / Bandits and online learning / Multi-armed bandits
Optimal Streaming Algorithms for Multi-Armed Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Randomizing the comparison threshold α (ε/4 with prob 1/(log j+1), else ε/2) in the single-arm ε-BAI algorithm | Streaming ε-BAI (Algorithm 1) · sample complexity | expected pulls | Helped |
| Using a single-arm memory and single pass with no assumption on ε (Algorithm 2) versus prior O(k)-memory method requiring ε ≤ Δ_{k+1} | Streaming ε-KAI · memory and sample complexity | memory | Helped |
| Converting exponential-gap elimination to streaming with ε-BAI selection subroutine | Streaming BAI (Algorithm 3) · passes | number of passes | Helped |
Closest papers by meaning
- Linear Streaming Bandit: Regret Minimization and Fixed-Budget Epsilon-Best Arm Identification (AAAI 2025 · similarity 0.92)
- Nearly Tight Bounds for Exploration in Streaming Multi-Armed Bandits with Known Optimality Gap (AAAI 2025 · similarity 0.92)
- Balancing Performance and Costs in Best Arm Identification (NeurIPS 2025 · similarity 0.90)
- Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids (ICLR 2025 · similarity 0.90)
- Optimal Multi-Fidelity Best-Arm Identification (NeurIPS 2024 · similarity 0.90)
- Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits (NeurIPS 2024 · similarity 0.90)
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