Research map / Bandits and online learning / Multi-armed bandits
Stochastic Rising Bandits
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 R-UCB for rising restless bandits | Stochastic rising restless bandits · Synthetic restless bandits | cumulative regret | Helped |
| Use R-UCB | Rising restless bandits · Synthetic restless bandits | cumulative regret | Hurt |
| Use R-UCB for rising rested bandits | Stochastic rising rested bandits · Synthetic rested bandits | cumulative regret | Helped |
| Use R-UCB instead of non-stationary bandit baselines | Rising rested bandits · Constructed two-arm rested bandit | cumulative regret | Helped |
| Use R-UCB with window ε = 1/32 | Online model selection · IMDB | empirical cumulative regret | Helped |
| Add a concavity assumption to non-decreasing rested bandits | Stochastic rising rested bandits · Rested bandits | regret | Helped |
Closest papers by meaning
- Best Arm Identification for Stochastic Rising Bandits (ICML 2024 · similarity 0.93)
- Tightening Regret Lower and Upper Bounds in Restless Rising Bandits (NeurIPS 2025 · similarity 0.92)
- Constrained Feedback Learning for Non-Stationary Multi-Armed Bandits (NeurIPS 2025 · similarity 0.90)
- Stochastic Bandits Robust to Adversarial Attacks (ICLR 2025 · similarity 0.89)
- Combinatorial Stochastic-Greedy Bandit (AAAI 2024 · similarity 0.89)
- Near-Optimal Stochastic Linear Bandits with Delay (NeurIPS 2026 · similarity 0.89)
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.