Entropy Search for Information-Efficient Global Optimization

Details Entropy Search for Information-Efficient Global Optimization Entropy Search for Information-Efficient Global Optimization

2011-12-06

arxiv.org/abs/1112.1217v1

Statistics

Machine Learning

Scientific paper

Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.

Affiliated with

Also associated with

No associations

Rating

Entropy Search for Information-Efficient Global Optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Entropy Search for Information-Efficient Global Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Entropy Search for Information-Efficient Global Optimization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-380839