- Basic setting is the stochastic (mutlidimensional) continuous bandit problem.
- The other competing algorithms for the domain at the time this paper was written don’t seem very sophisticated and are variants of gradient ascent.
- Uses a Bayesian method of locally weighted polynomial model, which gives a distribution over predictions for a query point.
- Noise is assumed, but the parameters are initially unknown (then learned).
- Claim the Bayesian approach allows for more reasonable behavior when the density of sampled points is low.
- The optimal answer is intractable, but four proposed approximation algorithms are listed, a comparison of those 4 and a few others are shown empirically in 2 different domains.