Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SupCB-GLM with stage-wise confidence filtering and independent sample sets | GLM contextual bandits with fixed K · GLM contextual bandits, fixed K | expected regret: Õ(d√T) -> O((log T)^1.5 √(dT log K)) | Helped |
| Use UCB-GLM with a confidence bonus | GLM contextual bandits · GLM contextual bandits | expected regret: Õ(d√T) | Helped |
| Assume the cumulative inverse square-root minimum eigenvalue is bounded by c√T | UCB-GLM · GLM contextual bandits under the stated eigenvalue condition | regret upper bound: Õ(d√T) -> (C Lμ σ/κ) √(dT log(T/δ)) | Helped |
| Use directional finite-sample confidence for the MLE instead of bounding only its parameter error in ℓ2 | SupCB-GLM regret analysis · GLM contextual bandits, fixed K | regret upper bound: removes an extra √d factor | Helped |
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- Second Order Bounds for Contextual Bandits with Function Approximation (ICLR 2025 · similarity 0.90)
- Variance-Aware Feel-Good Thompson Sampling for Contextual Bandits (NeurIPS 2025 · similarity 0.90)
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