Research map / Bandits and online learning / Multi-armed bandits
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-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 GLR-CUCB with GLRT change detection and CUCB | Piecewise-stationary combinatorial semi-bandits · Synthetic dataset | cumulative regret | Helped |
| Use GLR-CUCB with GLRT change detection and CUCB | Piecewise-stationary combinatorial semi-bandits · Yahoo! Experiment 1 | cumulative regret | Helped |
| Use local restart instead of restarting estimates for all base arms (ablation) | GLR-CUCB · Yahoo! Experiment 1 | cumulative regret | Helped |
| Use local restart instead of restarting estimates for all base arms (ablation) | GLR-CUCB · Synthetic dataset | cumulative regret | No clear effect |
Closest papers by meaning
- Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits (NeurIPS 2024 · similarity 0.89)
- Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation (NeurIPS 2026 · similarity 0.88)
- Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits (NeurIPS 2024 · similarity 0.88)
- Combinatorial Stochastic-Greedy Bandit (AAAI 2024 · similarity 0.88)
- Non-Stationary Lipschitz Bandits (NeurIPS 2025 · similarity 0.87)
- Oracle-Efficient Combinatorial Semi-Bandits (NeurIPS 2025 · 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.