Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Stochastic Bandits with Context Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Observe the realized context after choosing the action and use it for regression | UCB with context distributions · Synthetic Example, Movielens, Crop Yield Data | cumulative regret | Helped |
| Use sampled feature averages with L=100 instead of exact expected features (ablation) | UCB with context distributions · Synthetic Example, Movielens, Crop Yield Data | cumulative regret | No clear effect |
| Use the exact context realization before choosing the action | UCB contextual bandit · Synthetic Example, Movielens, Crop Yield Data | cumulative regret | Helped |
| Increase the feature-sampling size to L=100 (ablation) | Sampled-feature UCB · Synthetic Example, Movielens, Crop Yield Data | cumulative regret | Helped |
Closest papers by meaning
- Context-lumpable stochastic bandits (NeurIPS 2023 · similarity 0.93)
- Model Selection for Contextual Bandits (NeurIPS 2019 · similarity 0.92)
- Active Context Selection Improves Simple Regret in Contextual Bandits (NeurIPS 2026 · similarity 0.91)
- High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions (ICML 2025 · similarity 0.91)
- Contextual Bandits for Unbounded Context Distributions (ICML 2025 · similarity 0.90)
- Stochastic contextual bandits with graph feedback: from independence number to MAS number (NeurIPS 2024 · similarity 0.90)
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