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Bayesian optimization is popular for optimizing time-consuming black-box objectives.
The elements of integration
Bartle, R. G. (1966) · 1966
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Information Value Theory
Howard, R. (1966) · 1966
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The elements of integration
Bartle, R. G. (1966) · 1966
Earlier work this paper cites.
Information Value Theory
Howard, R. (1966) · 1966
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A unified view of the IPA, SF, and LR gradient estimation techniques
L’Ecuyer, P. (1990) · 1990
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A unified view of the IPA, SF, and LR gradient estimation techniques
L’Ecuyer, P. (1990) · 1990
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Differentiation of the cholesky algorithm
Smith, S. P. (1995) · 1995
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Differentiation of the cholesky algorithm
Smith, S. P. (1995) · 1995
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Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J. (1998) · 1998
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Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J. (1998) · 1998
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Envelope theorems for arbitrary choice sets
Milgrom, P. and Segal, I. (2002) · 2002
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Envelope theorems for arbitrary choice sets
Milgrom, P. and Segal, I. (2002) · 2002
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Stochastic approximation and recursive algorithms and applications
Kushner, H. and Yin, G. G. (2003) · 2003
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Stochastic approximation and recursive algorithms and applications
Kushner, H. and Yin, G. G. (2003) · 2003
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Sequential kriging optimization using multiple-fidelity evaluations
Huang, D., Allen, T., Notz, W., and Miller, R. (2006) · 2006
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Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K. I. (2006) · 2006
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Bayesian optimization with gradients
Wu, J., Poloczek, M., Wilson, A. G., and Frazier, P. I. (2017) · 2006
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Sequential kriging optimization using multiple-fidelity evaluations
Huang, D., Allen, T., Notz, W., and Miller, R. (2006) · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K. I. (2006) · 2006
Cited alongside, same era.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
Cited alongside, same era.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
Cited alongside, same era.
emcee: the mcmc hammer
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J. (2013) · 2013
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2016) · 2016
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documentation for gpml matlab code version 4.0
Rasmussen, C. E. and Nickisch, H. (2016) · 2016
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Parallel bayesian global optimization of expensive functions
Wang, J., Clark, S. C., Liu, E., and Frazier, P. I. (2016) · 2016
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The parallel knowledge gradient method for batch bayesian optimization
Wu, J. and Frazier, P. (2016) · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2016) · 2016
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emcee: the mcmc hammer
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J. (2013) · 2013
Cited alongside, same era.
Freeze-thaw bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P. (2014) · 2014
Cited alongside, same era.
Freeze-thaw bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P. (2014) · 2014
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, T., Springenberg, J. T., and Hutter, F. (2015) · 2015
Cited alongside, same era.
Towards efficient bayesian optimization for big data
Klein, A., Bartels, S., Falkner, S., Hennig, P., and Hutter, F. (2015) · 2015
Cited alongside, same era.
Multifidelity optimization using statistical surrogate modeling for non-hierarchical information sources
Lam, R., Allaire, D., and Willcox, K. (2015) · 2015
Cited alongside, same era.
documentation for gpml matlab code version 4.0
Rasmussen, C. E. and Nickisch, H. (2016) · 2016
Later among the works it cites.
Parallel bayesian global optimization of expensive functions
Wang, J., Clark, S. C., Liu, E., and Frazier, P. I. (2016) · 2016
Later among the works it cites.
The parallel knowledge gradient method for batch bayesian optimization
Wu, J. and Frazier, P. (2016) · 2016
Later among the works it cites.
Multi-fidelity bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Poczos, B. (2017) · 2017
Later among the works it cites.
Robo: A flexible and robust bayesian optimization framework in python
Klein, A., Falkner, S., Mansur, N., and Hutter, F. (2017b) · 2017
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Practical bayesian optimization for variable cost objectives
McLeod, M., Osborne, M. A., and Roberts, S. J. (2017) · 2017
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Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P. I. (2017) · 2017
Later among the works it cites.
Multi-fidelity bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Poczos, B. (2017) · 2017
Later among the works it cites.
Robo: A flexible and robust bayesian optimization framework in python
Klein, A., Falkner, S., Mansur, N., and Hutter, F. (2017b) · 2017
Later among the works it cites.
Practical bayesian optimization for variable cost objectives
McLeod, M., Osborne, M. A., and Roberts, S. J. (2017) · 2017
Later among the works it cites.
Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P. I. (2017) · 2017
Later among the works it cites.
Bayesian optimization with gradients
Wu, J., Poloczek, M., Wilson, A. G., and Frazier, P. I. (2017) · 2017
Later among the works it cites.