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Budgeted Optimization with Concurrent Stochastic-Duration Experiments. Javad Azimi , Alan Fern, Xiaoli Fern Oregon State University NIPS 2011. Bayesian Optimization (BO).
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Budgeted Optimization with Concurrent Stochastic-Duration Experiments JavadAzimi, 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 Gaussian Process Surface Current Experiments Select Single/multiple Experiment Run Experiment(s)
Extended BO Model Many domains include: • Ability to run concurrent experiments • Allowed to run a maximum of lconcurrent 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: • 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
