Research map / Bandits and online learning / Contextual bandits and Thompson sampling
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Knowledge gradient (KG) policy for Bayesian online logistic regression instead of baseline selection rules | Online Bayesian linear classifier with binary outcomes · UCI sonar, N=30 measurements | opportunity cost (lower is better) | Helped |
| KG policy instead of expected improvement (EI) for binary outputs | Online Bayesian logistic regression measurement selection · UCI datasets (glass, blood transfusion, breast cancer wpbc) | opportunity cost (lower is better) | Helped |
| KG policy instead of Thompson sampling | Online Bayesian logistic regression measurement selection · UCI and synthetic datasets, early iterations | opportunity cost (lower is better) | Helped |
| Uncertainty-based active learning (MostUncertain, Disc) instead of KG | Measurement selection for binary outcomes · UCI and synthetic datasets | opportunity cost (lower is better) | Mixed |
| Naive UCB on latent function w^T x instead of KG | Measurement selection for binary outcomes · UCI and synthetic datasets | opportunity cost (lower is better) | Hurt |
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