Budgeted optimization with concurrent stochastic duration experiments
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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) Experiments

  • 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 Experiments

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 Experiments

  • 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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