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High dimensional black-box optimization has broad applications but remains a challenging problem to solve.
On the distribution of points in a cube and the approximate evaluation of integrals
Il’ya Meerovich Sobol’ · 1967
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Letter to the editor—a monte carlo method for the approximate solution of certain types of constrained optimization problems
Martin Pincus · 1970
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Optimization of lipschitz continuous functions
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Alan E Gelfand and Adrian FM Smith · 1990
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Hit-and-run algorithms for generating multivariate distributions
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Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
Rainer Storn and Kenneth Price · 1997
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Fast forward selection to speed up sparse gaussian process regression
Matthias Seeger, Christopher Williams, and Neil Lawrence · 2003
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos · 2003
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Evolutionary optimization in uncertain environments-a survey
Yaochu Jin and Jürgen Branke · 2005
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Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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Eric Brochu, Vlad M Cora, and Nando De Freitas · 2010
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X-armed bandits
Sébastien Bubeck, Rémi Munos, Gilles Stoltz, and Csaba Szepesvári · 2011
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Optimistic optimization of a deterministic function without the knowledge of its smoothness
Rémi Munos · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sample-based planning for continuous action markov decision processes
Chris Mansley, Ari Weinstein, and Michael Littman · 2011
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Joint optimization and variable selection of high-dimensional gaussian processes
Bo Chen, Rui Castro, and Andreas Krause · 2012
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A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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Bandit-based planning and learning in continuous-action markov decision processes
Ari Weinstein and Michael L Littman · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Optimistic planning for continuous-action deterministic systems
Lucian Buşoniu, Alexander Daniels, Rémi Munos, and Robert Babuška · 2013
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Gaussian processes for big data
James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013
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Bayesian optimization in high dimensions via random embeddings
Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, and Nando De Freitas · 2013
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Bayesian multi-scale optimistic optimization
Ziyu Wang, Babak Shakibi, Lin Jin, and Nando Freitas · 2014
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From bandits to monte-carlo tree search: The optimistic principle applied to optimization and planning
Discovering and exploiting additive structure for bayesian optimization
Jacob Gardner, Chuan Guo, Kilian Weinberger, Roman Garnett, and Roger Grosse · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Towards generalization and simplicity in continuous control
Aravind Rajeswaran, Kendall Lowrey, Emanuel V Todorov, and Sham M Kakade · 2017
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A tutorial on bayesian optimization
Peter I Frazier · 2018
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Rémi Munos · 2014
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Machines tuning machines: Configuring distributed stream processors with bayesian optimization
Lorenz Fischer, Shen Gao, and Abraham Bernstein · 2015
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
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Bayesian optimization with exponential convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2015
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High dimensional bayesian optimisation and bandits via additive models
Kirthevasan Kandasamy, Jeff Schneider, and Barnabás Póczos · 2015
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A warped kernel improving robustness in bayesian optimization via random embeddings
Mickaël Binois, David Ginsbourger, and Olivier Roustant · 2015
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Bock: Bayesian optimization with cylindrical kernels
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Bohb: Robust and efficient hyperparameter optimization at scale
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High-dimensional bayesian optimization via additive models with overlapping groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, and Volkan Cevher · 2018
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Efficient high dimensional bayesian optimization with additivity and quadrature fourier features
Mojmir Mutny and Andreas Krause · 2018
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Simple random search provides a competitive approach to reinforcement learning
Horia Mania, Aurelia Guy, and Benjamin Recht · 2018
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Sample-efficient neural architecture search by learning action space
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, and Yuandong Tian · 2019
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Scalable global optimization via local bayesian optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek · 2019
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Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
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A framework for bayesian optimization in embedded subspaces
Amin Nayebi, Alexander Munteanu, and Matthias Poloczek · 2019
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2019
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Monte carlo tree search in continuous spaces using voronoi optimistic optimization with regret bounds
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Versatile black-box optimization
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