Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Thompson Sampling and Approximate Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| sample from over-dispersed approximation Q_t (KL(Pi_t,Q_t)=2) instead of exact posterior | Thompson sampling · 2-armed Gaussian bandit, T=100 | cumulative regret | Hurt |
| sample from under-dispersed approximation Z_t (KL(Z_t,Pi_t)=1.5) instead of exact posterior | Thompson sampling · 2-armed Gaussian bandit, T=100 | cumulative regret | Hurt |
| add forced exploration (rate 1/t) (ablation) | Thompson sampling with over-dispersed Q_t · 2-armed Gaussian bandit, T=100 | cumulative regret | No clear effect |
| add forced exploration (rate 1/t) (ablation) | Thompson sampling with under-dispersed Z_t and Ensemble sampling (M=2) · 2-armed Gaussian bandit, T=100 | cumulative regret | Helped |
| add forced exploration (rate 50/t) (ablation) | Mean-field VI and Ensemble sampling (M=5) · 50-armed Gaussian bandit, T=3000 | cumulative regret | Helped |
Closest papers by meaning
- What Does Thompson Sampling Optimize? (ICML 2026 · similarity 0.92)
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- The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models (AAAI 2024 · similarity 0.90)
- $\mathtt{VITS}$ : Variational Inference Thompson Sampling for contextual bandits (ICML 2024 · similarity 0.89)
- Adaptive Regret for Bandits Made Possible: Two Queries Suffice (ICLR 2024 · similarity 0.89)
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