Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Design of Experiments for Stochastic Contextual Linear Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the offline-context planner to construct a stochastic exploration policy instead of choosing actions uniformly at random | Planner-Sampler · Yahoo! Learning to Rank | policy value | Helped |
| Use the offline-context planner to construct a stochastic exploration policy instead of choosing actions uniformly at random | Planner-Sampler · Synthetic contextual bandit | policy value | Helped |
| Choose actions by largest feature norm rather than using the planner-sampler policy | Synthetic contextual bandit · Synthetic contextual bandit | policy value | Hurt |
| Always choose a single action rather than using the planner-sampler policy | Synthetic contextual bandit · Synthetic contextual bandit | policy value | Hurt |
| Use regularization λ=1 (ablation) | Planner-Sampler and random sampler · Yahoo! Learning to Rank | policy value | Helped |
| Increase regularization to λ=10 (ablation) | Planner-Sampler · Yahoo! Learning to Rank | policy value | Mixed |
| Decrease regularization to λ=0.1 (ablation) | Planner-Sampler · Yahoo! Learning to Rank | policy value | Mixed |
| Use α<1 for the planner covariance updates (ablation) | Planner-Sampler · Yahoo! Learning to Rank | policy value | No clear effect |
Closest papers by meaning
- Stochastic Bandits with Context Distributions (NeurIPS 2019 · similarity 0.92)
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- Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates (ICML 2026 · similarity 0.91)
- Causal Contextual Bandits with Targeted Interventions (ICLR 2022 · similarity 0.90)
- Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates (NeurIPS 2024 · similarity 0.89)
- Active Context Selection Improves Simple Regret in Contextual Bandits (NeurIPS 2026 · similarity 0.89)
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