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We are interested in derivative-free optimization of high-dimensional functions.
Random optimization
J Matyas · 1965
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Evolutionsstrategie - optimierung technischer systeme nach prinzipien der biologischen information
Ingo Rechenberg · 1973
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On tail probabilities for martingales
David A Freedman · 1975
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XFOIL: An analysis and design system for low Reynolds number airfoils
Mark Drela · 1989
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Benchmarking optimization software with performance profiles
Elizabeth D. Dolan and Jorge J. Moré · 2002
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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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Online convex optimization in the bandit setting: Gradient descent without a gradient
Abraham Flaxman, Adam Tauman Kalai, and H. Brendan McMahan · 2005
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Efficient algorithms for online decision problems
Adam Tauman Kalai and Santosh Vempala · 2005
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Introduction to Derivative-Free Optimization
Andrew R Conn, Katya Scheinberg, and Luis N Vicente · 2009
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On the generalization ability of online strongly convex programming algorithms
Sham M Kakade and Ambuj Tewari · 2009
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Optimal algorithms for online convex optimization with multi-point bandit feedback
Alekh Agarwal, Ofer Dekel, and Lin Xiao · 2010
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Kriging is well-suited to parallelize optimization
David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro · 2010
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Exponential natural evolution strategies
Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, and Jürgen Schmidhuber · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias W. Seeger · 2010
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Query complexity of derivative-free optimization
Kevin G. Jamieson, Robert D. Nowak, and Benjamin Recht · 2012
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2012
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Joe Staines and David Barber · 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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High-dimensional Gaussian process bandits
Josip Djolonga, Andreas Krause, and Volkan Cevher · 2013
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Stochastic first- and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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Handbook of Monte Carlo Methods
Dirk P Kroese, Thomas Taimre, and Zdravko I Botev · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
Ohad Shamir · 2013
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
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Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Structured evolution with compact architectures for scalable policy optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, and Adrian Weller · 2018
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An accelerated method for derivative-free smooth stochastic convex optimization
Pavel Dvurechensky, Alexander Gasnikov, and Eduard Gorbunov · 2018
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Optimal rates for zero-order convex optimization: The power of two function evaluations
John C. Duchi, Michael I. Jordan, Martin J. Wainwright, and Andre Wibisono · 2015
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Differentiating the multipoint expected improvement for optimal batch design
Sébastien Marmin, Clément Chevalier, and David Ginsbourger · 2015
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Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
Andrew Gordon Wilson and Hannes Nickisch · 2015
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Highly-smooth zero-th order online optimization
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Manifold Gaussian processes for regression
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The CMA evolution strategy: A tutorial
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GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration
Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson · 2018
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Stable baselines
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Safe mutations for deep and recurrent neural networks through output gradients
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Simple random search of static linear policies is competitive for reinforcement learning
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High-dimensional Bayesian optimization via additive models with overlapping groups
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Esa/Pagmo2: Pagmo 2.10, 2019
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From complexity to simplicity: Adaptive ES-active subspaces for blackbox optimization
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, and Vikas Sindhwani · 2019
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CMA-ES/pycma on Github
Nikolaus Hansen, Youhei Akimoto, and Petr Baudis · 2019
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Adaptive and safe Bayesian optimization in high dimensions via one-dimensional subspaces
Johannes Kirschner, Mojmír Mutný, Nicole Hiller, Rasmus Ischebeck, and Andreas Krause · 2019
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Guided evolutionary strategies: Escaping the curse of dimensionality in random search
Niru Maheswaranathan, Luke Metz, George Tucker, and Jascha Sohl-Dickstein · 2019
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Contrasting exploration in parameter and action space: A zeroth-order optimization perspective
Anirudh Vemula, Wen Sun, and J. Andrew Bagnell · 2019
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