Corpus ID: 43194389

Evaluation System for a Bayesian Optimization Service

@article{Dewancker2016EvaluationSF,
  title={Evaluation System for a Bayesian Optimization Service},
  author={Ian Dewancker and M. McCourt and Scott C. Clark and Patrick Hayes and A. Johnson and George Ke},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.06170}
}
Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires careful testing methodology and sound statistical analysis. In this talk we will outline our development of an evaluation framework to rigorously test and measure the impact of changes to the SigOpt optimization service. We present an overview of our evaluation system and discuss how this framework empowers our… Expand
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References

SHOWING 1-10 OF 14 REFERENCES
A Stratified Analysis of Bayesian Optimization Methods
A Strategy for Ranking Optimization Methods using Multiple Criteria
Practical Bayesian Optimization of Machine Learning Algorithms
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Sequential Model-Based Optimization for General Algorithm Configuration
Algorithms for Hyper-Parameter Optimization
pyswarm : Particle swarm optimization (PSO) with constraint support. https://github. com/tisimst/pyswarm
  • pyswarm : Particle swarm optimization (PSO) with constraint support. https://github. com/tisimst/pyswarm
  • 2014
Optimization Test Functions
  • https: //github.com/sigopt/evalset,
  • 2016
Optimization Test Functions. https: //github.com/sigopt/evalset
  • Optimization Test Functions. https: //github.com/sigopt/evalset
  • 2016
MOE: A global, black box optimization engine for real world metric optimization
  • https://github.com/Yelp/MOE,
  • 2014
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