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Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate.
A stochastic approximation method
Robbins, H. and Monro, S. (1951) · 1951
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Multidimensional stochastic approximation methods
Blum, J. R. (1954) · 1954
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The greatest of a finite set of random variables
Clark, C. E. (1961) · 1961
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A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Kushner, H. J. (1964) · 1964
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On Bayesian methods for seeking the extremum
Močkus, J. (1975) · 1975
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Single-step Bayesian search method for an extremum of functions of a single variable
Žilinskas, A. (1975) · 1975
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The application of Bayesian methods for seeking the extremum
Močkus, J., Tiesis, V., and Žilinskas, A. (1978) · 1978
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Controlled Markov Processes
Dynkin, E. and Yushkevich, A. (1979) · 1979
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Infinitesimal and finite perturbation analysis for queueing networks
Ho, Y.-C., Cao, X., and Cassandras, C. (1983) · 1983
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On the limited memory BFGS method for large scale optimization
Liu, D. C. and Nocedal, J. (1989) · 1989
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Bayesian Approach to Global Optimization: Theory and Applications
Močkus, J. (1989) · 1989
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Bayesian approach to global optimization and application to multiobjective and constrained problems
Močkus, J. and Močkus, L. (1991) · 1991
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Reinforcement learning: A survey
Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996) · 1996
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Average performance of a class of adaptive algorithms for global optimization
Calvin, J. (1997) · 1997
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Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J. (1998) · 1998
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Global versus local search in constrained optimization of computer models
Schonlau, M., Welch, W. J., and Jones, D. R. (1998) · 1998
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (1998) · 1998
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A rigorous framework for optimization of expensive functions by surrogates
Booker, A., Dennis, J., Frank, P., Serafini, D., Torczon, V., and Trosset, M. (1999) · 1999
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On the convergence of the P-algorithm for one-dimensional global optimization of smooth functions
Calvin, J. and Žilinskas, A. (1999) · 1999
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One-dimensional P-algorithm with convergence rate O(n-3+ δ \delta ) for smooth functions
Calvin, J. and Žilinskas, A. (2000) · 2000
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Sequential design of computer experiments to minimize integrated response functions
Williams, B. J., Santner, T. J., and Notz, W. I. (2000) · 2000
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Design and analysis of robust total joint replacements: finite element model experiments with environmental variables
Chang, P. B., Williams, B. J., Bhalla, K. S. B., Belknap, T. W., Santner, T. J., Notz, W. I., and Bartel, D. L. (2001) · 2001
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New two-stage and sequential procedures for selecting the best simulated system
Chick, S. E. and Inoue, K. (2001) · 2001
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A family of algorithms for approximate Bayesian inference
Minka, T. P. (2001) · 2001
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Envelope theorems for arbitrary choice sets
Milgrom, P. and Segal, I. (2002) · 2002
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Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations
Sasena, M. (2002) · 2002
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Slice sampling
Neal, R. M. (2003) · 2003
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A parallel updating scheme for approximating and optimizing high fidelity computer simulations
Sóbester, A., Leary, S., and Keane, A. (2004) · 2004
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One-dimensional global optimization for observations with noise
Calvin, J. and Žilinskas, A. (2005) · 2005
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Constrained global optimization of expensive black box functions using radial basis functions
Regis, R. and Shoemaker, C. (2005) · 2005
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Sequential kriging optimization using multiple-fidelity evaluations
Huang, D., Allen, T., Notz, W., and Miller, R. (2006) · 2006
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Statistical improvement criteria for use in multiobjective design optimization
Keane, A. (2006) · 2006
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ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
Knowles, J. (2006) · 2006
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Gaussian Processes for Machine Learning
Rasmussen, C. and Williams, C. (2006) · 2006
Cited alongside, same era.
Multi-fidelity optimization via surrogate modelling
Forrester, A. I., Sóbester, A., and Keane, A. J. (2007) · 2007
Cited alongside, same era.
A multi-points criterion for deterministic parallel global optimization based on kriging
Ginsbourger, D., Le Riche, R., and Carraro, L. (2007) · 2007
Cited alongside, same era.
Most likely heteroscedastic gaussian process regression
Kersting, K., Plagemann, C., Pfaff, P., and Burgard, W. (2007) · 2007
Cited alongside, same era.
Automatic gait optimization with Gaussian process regression
Lizotte, D., Wang, T., Bowling, M., and Schuurmans, D. (2007) · 2007
Cited alongside, same era.
Approximate Dynamic Programming: Solving the Curses of Dimensionality
Powell, W. B. (2007) · 2007
Cited alongside, same era.
Bisection search with noisy responses
Waeber, R., Frazier, P. I., and Henderson, S. G. (2013) · 2013
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Bayesian optimization in high dimensions via random embeddings
Wang, Z., Zoghi, M., Hutter, F., Matheson, D., De Freitas, N., et al. (2013) · 2013
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Sequential Bayes-optimal policies for multiple comparisons with a known standard
Xie, J. and Frazier, P. I. (2013) · 2013
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Bayesian optimization with inequality constraints
Gardner, J. R., Kusner, M. J., Xu, Z. E., Weinberger, K. Q., and Cunningham, J. P. (2014) · 2014
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Bayesian Data Analysis
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014) · 2014
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Predictive entropy search for efficient global optimization of black-box functions
Hernández-Lobato, J. M., Hoffman, M. W., and Ghahramani, Z. (2014) · 2014
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Watershed calibration using multistart local optimization and evolutionary optimization with radial basis function approximation/calage au niveau du bassin versant à l’aide d’une optimisation locale à démarrage multiple et d’une optimisation évolutive avec approximation à fonctions de base radiale
Shoemaker, C., Regis, R., and Fleming, R. (2007) · 2007
Cited alongside, same era.
Engineering Design via Surrogate Modelling: A Practical Guide
Forrester, A., Sóbester, A., and Keane, A. (2008) · 2008
Cited alongside, same era.
A knowledge-gradient policy for sequential information collection
Frazier, P. I., Powell, W. B., and Dayanik, S. (2008) · 2008
Cited alongside, same era.
Design and Analysis of Simulation Experiments
Kleijnen, J. P. et al. (2008) · 2008
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Practical Bayesian Optimization
Lizotte, D. (2008) · 2008
Cited alongside, same era.
Multi-armed bandit problems
Mahajan, A. and Teneketzis, D. (2008) · 2008
Cited alongside, same era.
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Discrete optimization via simulation using Gaussian Markov random fields
Salemi, P., Nelson, B. L., and Staum, J. (2014) · 2014
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Input warping for Bayesian optimization of non-stationary functions
Snoek, J., Swersky, K., Zemel, R., and Adams, R. (2014) · 2014
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Freeze-thaw bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P. (2014) · 2014
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Predictive entropy search for bayesian optimization with unknown constraints
Hernández-Lobato, J. M., Gelbart, M. A., Hoffman, M. W., Adams, R. P., and Ghahramani, Z. (2015) · 2015
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High dimensional bayesian optimisation and bandits via additive models
Kandasamy, K., Schneider, J., and Póczos, B. (2015) · 2015
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Multifidelity optimization using statistical surrogate modeling for non-hierarchical information sources
Lam, R., Allaire, D. L., and Willcox, K. E. (2015) · 2015
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Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization
Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L., and Tanaka, I. (2015) · 2015
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Do we need “harmless” Bayesian optimization and “first-order” Bayesian optimization
Ahmed, M. O., Shahriari, B., and Schmidt, M. (2016) · 2016
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Multi-step Bayesian optimization for one-dimensional feasibility determination
Cashore, J. M., Kumarga, L., and Frazier, P. I. (2016) · 2016
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Bayesian optimization for materials design
Frazier, P. I. and Wang, J. (2016) · 2016
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GLASSES: Relieving the myopia of bayesian optimisation
González, J., Osborne, M., and Lawrence, N. (2016) · 2016
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Gaussian process bandit optimisation with multi-fidelity evaluations
Kandasamy, K., Dasarathy, G., Oliva, J. B., Schneider, J., and Póczos, B. (2016) · 2016
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Fast Bayesian optimization of machine learning hyperparameters on large datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F. (2016) · 2016
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Bayesian optimization with a finite budget: An approximate dynamic programming approach
Lam, R., Willcox, K., and Wolpert, D. H. (2016) · 2016
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Multi-start methods
Martí, R., Lozano, J. A., Mendiburu, A., and Hernando, L. (2016) · 2016
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Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N. (2016) · 2016
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COMBO: An efficient Bayesian optimization library for materials science
Ueno, T., Rhone, T. D., Hou, Z., Mizoguchi, T., and Tsuda, K. (2016) · 2016
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The parallel knowledge gradient method for batch Bayesian optimization
Wu, J. and Frazier, P. (2016) · 2016
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Designing nanostructures for phonon transport via Bayesian optimization
Ju, S., Shiga, T., Feng, L., Hou, Z., Tsuda, K., and Shiomi, J. (2017) · 2017
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Practical bayesian optimization for variable cost objectives
McLeod, M., Osborne, M. A., and Roberts, S. J. (2017) · 2017
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Bayesian Optimization for Materials Science
Packwood, D. (2017) · 2017
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Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P. (2017) · 2017
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Bayesian optimization with gradients
Wu, J., Poloczek, M., Wilson, A. G., and Frazier, P. (2017) · 2017
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Efficient global optimisation for black-box simulation via sequential intrinsic kriging
Mehdad, E. and Kleijnen, J. P. (2018) · 2018
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Twitter acquires machine learning startup whetlab
Perez, S. (2015) · 2018
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Bayesian optimization with expensive integrands
Toscano-Palmerin, S. and Frazier, P. I. (2018) · 2018
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