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

2. 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)

3. 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

4. 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