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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

Budgeted Optimization with Concurrent Stochastic-Duration Experiments

JavadAzimi, Alan Fern, Xiaoli Fern

Oregon State University

NIPS 2011

bayesian optimization bo
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
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
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

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