Ideas from: Memory-based Stochastic Optimization; Moore, Schneider (NIPS 1995)


  1. Basic setting is the stochastic (mutlidimensional) continuous bandit problem.
  2. 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.
  3. Uses a Bayesian method of locally weighted polynomial model, which gives a distribution over predictions for a query point.
  4. Noise is assumed, but the parameters are initially unknown (then learned).
  5. Claim the Bayesian approach allows for more reasonable behavior when the density of sampled points is low.
  6. 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.
Advertisements
Tagged , , ,

One thought on “Ideas from: Memory-based Stochastic Optimization; Moore, Schneider (NIPS 1995)

  1. Michael Littman says:

    I guess one important difference between stochastic optimization and bandit problems is how performance is meaasured. Bandit problems care about performance during learning/search. Optimization does not.

    If we continue to use upper confidence intervals, it would be good to understand how the local-weighted methods provide them.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: