Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Semiparametric Contextual Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BOSE instead of linear-bandit and agnostic baselines | Synthetic linear environment, d=10, K=2 · Linear synthetic environment | cumulative regret | Hurt |
| Use BOSE instead of OFUL, Thompson sampling, and agnostic baselines | Synthetic semiparametric contextual bandits · Confounded synthetic environment with sphere features | cumulative regret | Helped |
| Use BOSE instead of OFUL | Synthetic semiparametric contextual bandits · Confounded synthetic environment with positive-orthant features | cumulative regret | Helped |
Closest papers by meaning
- Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates (ICML 2026 · similarity 0.93)
- Model Selection for Contextual Bandits (NeurIPS 2019 · similarity 0.92)
- Second Order Bounds for Contextual Bandits with Function Approximation (ICLR 2025 · similarity 0.91)
- Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit (NeurIPS 2024 · similarity 0.91)
- Contextual Bandits for Unbounded Context Distributions (ICML 2025 · similarity 0.91)
- Mixed-Effects Contextual Bandits (AAAI 2024 · similarity 0.91)
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