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Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process.
Lectures on Fourier Integrals
S. Bochner · 1959
Earlier work this paper cites.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
H.J. Kushner · 1964
Earlier work this paper cites.
On Bayesian methods for seeking the extremum
J. Močkus · 1975
Earlier work this paper cites.
Accelerated greedy algorithms for maximizing submodular set functions
M. Minoux · 1978
Earlier work this paper cites.
An analysis of approximations for maximizing submodular set functions—I
G.L. Nemhauser, L.A. Wolsey, and M.L. Fisher · 1978
Earlier work this paper cites.
Convergence of parameter sensitivity estimates in a stochastic experiment
X. Cao · 1985
Earlier work this paper cites.
Performance continuity and differentiability in Monte Carlo optimization
P. Glasserman · 1988
Earlier work this paper cites.
Numerical computation of multivariate normal probabilities
A. Genz · 1992
Earlier work this paper cites.
Lipschitzian optimization without the Lipschitz constant
D.R. Jones, C.D. Perttunen, and B.E. Stuckman · 1993
Earlier work this paper cites.
Application of Bayesian approach to numerical methods of global and stochastic optimization
J. Močkus · 1994
Earlier work this paper cites.
Efficient global optimization of expensive black box functions
D. Jones, M. Schonlau, and W. Welch · 1998
Earlier work this paper cites.
Computing multivariate normal probabilities: A new look
H.I. Gassmann, I. Deák, and T. Szántai · 2002
Earlier work this paper cites.
Numerical computation of rectangular bivariate and trivariate normal and t probabilities
A. Genz · 2004
Earlier work this paper cites.
Multivariate t-distributions and their applications
S. Kotz and S. Nadarajah · 2004
Earlier work this paper cites.
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M.H. DeGroot · 2005
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C.E. Rasmussen and C.K.I. Williams · 2006
Earlier work this paper cites.
The Bayesian choice: from decision-theoretic foundations to computational implementation
R. Christian · 2007
Earlier work this paper cites.
The tradeoffs of large scale learning
O. Bousquet and L. Bottou · 2008
Earlier work this paper cites.
Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2008
Earlier work this paper cites.
Gaussian processes for global optimization
M.A. Osborne, R. Garnett, and S.J. Roberts · 2009
Earlier work this paper cites.
Batch Bayesian optimization via simulation matching
J. Azimi, A. Fern, and X.Z. Fern · 2010
Earlier work this paper cites.
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N. Srinivas, A. Krause, S. Kakade, and M. Seeger · 2010
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J.P. Cunningham, P. Hennig, and S. Lacoste-Julien · 2011
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D. Ginsbourger, J. Janusevskis, and R. Le Riche · 2011
Cited alongside, same era.
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A. Krause and D. Golovin · 2014
Later among the works it cites.
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R. Martinez-Cantin · 2014
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D.J. Rezende, M. Shakir, and D. Wierstra · 2014
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A. Shah and Z. Ghahramani · 2015
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F. Hutter, H.H. Hoos, and K. Leyton-Brown · 2011
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J. Bergstra and Y. Bengio · 2012
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P. Hennig and C. Schuler · 2012
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Practical Bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R.P. Adams · 2012
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Learning with submodular functions: A convex optimization perspective
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Fast computation of the multi-points expected improvement with applications in batch selection
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S. Bansal, R. Calandra, T. Xiao, S. Levine, and C.J. Tomlin · 2017
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Z. Wang and S. Jegelka · 2017
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J. Wu, M. Poloczek, A.G. Wilson, and P.I. Frazier · 2017
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