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Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space.
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Completely derandomized self-adaptation in evolution strategies
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
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Nesterov, Y · 2004
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A derivative-free nonmonotone line-search technique for unconstrained optimization
Diniz-Ehrhardt, M., Martínez, J., and Raydán, M · 2008
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Information consistency of nonparametric gaussian process methods
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Gaussian process optimization in the bandit setting: No regret and experimental design
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Online Learning for Linearly Parametrized Control Problems
Abbasi-Yadkori, Y · 2012
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GPy: A gaussian process framework in python
GPy · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
Nesterov, Y · 2012
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Stochastic Recursive Algorithms for Optimization
Bhatnagar, S., Prasad, H., and Prashanth, L · 2013
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High-dimensional Gaussian process bandits
Djolonga, J., Krause, A., and Cevher, V · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
Shamir, O · 2013
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Direct search based on probabilistic descent
Gratton, S., Royer, C. W., Vicente, L. N., and Zhang, Z · 2015
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Lower bounds on regret for noisy gaussian process bandit optimization
Scarlett, J., Bogunovic, I., and Cevher, V · 2017
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Max-value entropy search for efficient Bayesian optimization
Wang, Z. and Jegelka, S · 2017
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Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
Cartis, C. and Scheinberg, K · 2018
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Gaussian processes and kernel methods: A review on connections and equivalences
Kanagawa, M., Hennig, P., Sejdinovic, D., and Sriperumbudur, B. K · 2018
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Information directed sampling and bandits with heteroscedastic noise
Kirschner, J. and Krause, A · 2018
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Safe exploration for optimization with gaussian processes
Sui, Y., Gotovos, A., Burdick, J., and Krause, A · 2015
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Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings
Qian, H., Hu, Y.-Q., and Yu, Y · 2016
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Taking the human out of the loop: A review of bayesian optimization
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Probabilistic line searches for stochastic optimization
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A stochastic line search method with convergence rate analysis
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High-dimensional bayesian optimization via additive models with overlapping groups
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Tight regret bounds for Bayesian optimization in one dimension
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Schneider, P.-I., Garcia Santiago, X., Soltwisch, V., Hammerschmidt, M., Burger, S., and Rockstuhl, C · 2018
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Stagewise safe bayesian optimization with gaussian processes
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High dimensional bayesian optimization using dropout
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