Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Fern Oregon State University NIPS 2011

Bayesian Optimization (BO) Goal: Maximize an unknown function f by requesting a small set of function evaluations (experiments)—experiments are costly BO assumes prior over f – select next experiment based on posterior Traditional BO selects one experiment at a time Current Experiments Gaussian Process Surface Select Single/multiple Experiment Run Experiment(s)

Extended BO Model Many domains include: Ability to run concurrent experiments Allowed to run a maximum of l concurrent experiments Uncertainty about experiment durations Experiments have stochastic durations with known distribution P Total experimental budget n Experimental time horizon h Current BO models do not model these domain features

Proposed Solution Problem: Proposed Solutions: Poster Number: W052 Schedule when to start new experiments and which ones to start. Proposed Solutions: Offline Schedule: The start times have been defined before starting running experiments Online Schedule: The start times determine at each time step Poster Number: W052