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We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models.
An Automatic Method for Finding the Greatest or Least Value of a Function
H. H. Rosenbrock · 1960
Earlier work this paper cites.
Hedonic housing prices and the demand for clean air
David Harrison and Daniel Rubinfeld · 1978
Earlier work this paper cites.
Latin hypercube sampling (program user’s guide). [LHC, in FORTRAN]
Ronald Iman, James Davenport, and D. Zeigler · 1980
Earlier work this paper cites.
Newton’s method with a model trust region modification
D. C. Sorensen · 1982
Earlier work this paper cites.
The method of moving asymptotes—a new method for structural optimization
Krister Svanberg · 1987
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Genetic Programming: An Introduction
W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone · 1998
Earlier work this paper cites.
Principles of Optimal Design: Modeling and Computation
Panos Y. Papalambros and Douglass J. Wilde · 2000
Earlier work this paper cites.
Principles of optimal design: Modeling and computation p. y. papalambros and d. j. wilde second edition. cambridge university press, the edinburgh building, cambridge cb2 2ru, uk. 2000. 390pp. illustrated. £27.95. isbn 0-521-62727-3
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Earlier work this paper cites.
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S. Agostinelli et al · 2003
Earlier work this paper cites.
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Torbjörn Sjöstrand, Stephen Mrenna, and Peter Skands · 2008
Earlier work this paper cites.
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Mark J Abraham and Jill E Gready · 2011
Earlier work this paper cites.
The fairroot framework
M Al-Turany, D Bertini, R Karabowicz, D Kresan, P Malzacher, T Stockmanns, and F Uhlig · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
High-dimensional gaussian process bandits
Josip Djolonga, Andreas Krause, and Volkan Cevher · 2013
Earlier work this paper cites.
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Momin Jamil and Xin-She Yang · 2013
Earlier work this paper cites.
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Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
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Freek Stulp and Olivier Sigaud · 2013
Earlier work this paper cites.
Bayesian optimization in high dimensions via random embeddings
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Earlier work this paper cites.
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Roman Garnett, Michael A. Osborne, and Philipp Hennig · 2014
Earlier work this paper cites.
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Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Diederik P. Kingma and Jimmy Ba · 2014
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
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FFJORD: free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, and David Duvenaud · 2018
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Likelihood-free inference with emulator networks
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Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, and Jascha Sohl-Dickstein · 2018
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Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic besov space
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High dimensional bayesian optimization via supervised dimension reduction
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