Computer Science – Learning
Scientific paper
2010-09-28
Computer Science
Learning
This revision contains an updated the performance bound and other minor text changes
Scientific paper
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.
Brochu Eric
Freitas Nando de
Hoffman Matthew W.
No associations
LandOfFree
Portfolio Allocation for Bayesian 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 Portfolio Allocation for Bayesian Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Portfolio Allocation for Bayesian Optimization will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-692998