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Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions.
Note: On the interchange of derivative and expectation for likelihood ratio derivative estimators
P. L’Ecuyer · 1995
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Efficient global optimization of expensive black-box functions
D. R. Jones, M. Schonlau, and W. J. Welch · 1998
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The mnist database of handwritten digits, 1998
Y. LeCun, C. Cortes, and C. J. Burges · 1998
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Re-engineering the design process through computation
A. Jameson · 1999
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Envelope theorems for arbitrary choice sets
P. Milgrom and I. Segal · 2002
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Stochastic approximation and recursive algorithm and applications
J. Harold, G. Kushner, and G. Yin · 2003
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Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms , volume 97
J. Snyman · 2005
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Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
D. Huang, T. T. Allen, W. I. Notz, and N. Zeng · 2006
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A review of the adjoint-state method for computing the gradient of a functional with geophysical applications
R.-É. Plessix · 2006
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Engineering design via surrogate modelling: a practical guide
A. Forrester, A. Sobester, and A. Keane · 2008
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Practical bayesian optimization
D. J. Lizotte · 2008
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The knowledge-gradient policy for correlated normal beliefs
P. Frazier, W. Powell, and S. Dayanik · 2009
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Gaussian processes for global optimization
M. A. Osborne, R. Garnett, and S. J. Roberts · 2009
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E. Brochu, V. M. Cora, and N. De Freitas · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
N. Srinivas, A. Krause, M. Seeger, and S. M. Kakade · 2010
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The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression
W. Scott, P. Frazier, and W. Powell · 2011
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Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Parallel gaussian process optimization with upper confidence bound and pure exploration
E. Contal, D. Buffoni, A. Robicquet, and N. Vayatis · 2013
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emcee: the mcmc hammer
D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman · 2013
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Quantile-based optimization of noisy computer experiments with tunable precision
Kernel interpolation for scalable structured gaussian processes (kiss-gp)
A. G. Wilson and H. Nickisch · 2015
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Thoughts on massively scalable gaussian processes
A. G. Wilson, C. Dann, and H. Nickisch · 2015
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Do we need “harmless” bayesian optimization and “first-order” bayesian optimization?
M. O. Ahmed, B. Shahriari, and M. Schmidt · 2016
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NYC Trip Record Data
N. T. . L. Commission · 2016
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Batch bayesian optimization via local penalization
J. Gonzalez, Z. Dai, P. Hennig, and N. Lawrence · 2016
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Batched gaussian process bandit optimization via determinantal point processes
T. Kathuria, A. Deshpande, and P. Kohli · 2016
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V. Picheny, D. Ginsbourger, Y. Richet, and G. Caplin · 2013
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Multi-task bayesian optimization
K. Swersky, J. Snoek, and R. P. Adams · 2013
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Gaussian process kernels for pattern discovery and extrapolation
A. G. Wilson and R. P. Adams · 2013
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Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization
T. Desautels, A. Krause, and J. W. Burdick · 2014
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Bayesian optimization with inequality constraints
J. R. Gardner, M. J. Kusner, Z. E. Xu, K. Q. Weinberger, and J. Cunningham · 2014
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Bayesian optimization with unknown constraints
M. Gelbart, J. Snoek, and R. Adams · 2014
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Predictive entropy search for efficient global optimization of black-box functions
J. M. Hernández-Lobato, M. W. Hoffman, and Z. Ghahramani · 2014
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Minimum energy path calculations with gaussian process regression
O.-P. Koistinen, E. Maras, A. Vehtari, and H. Jónsson · 2016
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Efficient batch-sequential bayesian optimization with moments of truncated gaussian vectors
S. Marmin, C. Chevalier, and D. Ginsbourger · 2016
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Hyperparameter optimization with approximate gradient
F. Pedregosa · 2016
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Taking the human out of the loop: A review of bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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Parallel bayesian global optimization of expensive functions
J. Wang, S. C. Clark, E. Liu, and P. I. Frazier · 2016
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Deep kernel learning
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing · 2016
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The parallel knowledge gradient method for batch bayesian optimization
J. Wu and P. Frazier · 2016
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Multi-information source optimization
M. Poloczek, J. Wang, and P. I. Frazier · 2017
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