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Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly.
On a measure of the information provided by an experiment
Lindley, D. V. (1956) · 1956
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The existence of a utility function to represent preferences
Rader, T. (1963) · 1963
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Information-Based Objective Functions for Active Data Selection
MacKay, D. J. C. (1992) · 1992
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Signature verification using a” siamese” time delay neural network
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., and Shah, R. (1993) · 1993
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Bayesian experimental design: A review
Chaloner, K. and Verdinelli, I. (1995) · 1995
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Multitask learning
Caruana, R. (1997) · 1997
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The effectiveness of alternative preference elicitation methods in the analytic hierarchy process
Millet, I. (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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Catastrophic forgetting in connectionist networks
French, R. M. (1999) · 1999
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Incorporating expert prior knowledge into experimental design via posterior sampling
Li, C., Gupta, S., Rana, S., Nguyen, V., Robles-Kelly, A., and Venkatesh, S. (2020) · 2002
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Preference learning with gaussian processes
Chu, W. and Ghahramani, Z. (2005) · 2005
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Active preference learning with discrete choice data
Brochu, E., Freitas, N. D., and Ghosh, A. (2008) · 2008
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Brochu, E., Cora, V. M., and De Freitas, N. (2010) · 2010
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M. (2011) · 2011
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Active Learning
Settles, B. (2012) · 2012
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Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Deep Bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z. (2017) · 2017
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Preferential Bayesian Optimization
González, J., Dai, Z., Damianou, A., and Lawrence, N. D. (2017) · 2017
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Phoenics: a Bayesian optimizer for chemistry
Hase, F., Roch, L. M., Kreisbeck, C., and Aspuru-Guzik, A. (2018) · 2018
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Human strategic steering improves performance of interactive optimization
Colella, F., Daee, P., Jokinen, J., Oulasvirta, A., and Kaski, S. (2020) · 2020
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Projective preferential bayesian optimization
Mikkola, P., Todorović, M., Järvi, J., Rinke, P., and Kaski, S. (2020) · 2020
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Incorporating expert prior in bayesian optimisation via space warping
Ramachandran, A., Gupta, S., Rana, S., Li, C., and Venkatesh, S. (2020) · 2020
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Shah, N. B., Balakrishnan, S., Bradley, J., Parekh, A., Ramchandran, K., and Wainwright, M. (2014) · 2014
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015) · 2015
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Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., Salakhutdinov, R., et al. (2015) · 2015
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Scalable Bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R. (2015) · 2015
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Fully-convolutional siamese networks for object tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., and Torr, P. H. (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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Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F. (2016) · 2016
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Multi-fidelity Bayesian optimization with max-value entropy search and its parallelization
Takeno, S., Fukuoka, H., Tsukada, Y., Koyama, T., Shiga, M., Takeuchi, I., and Karasuyama, M. (2020) · 2020
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Bayesian optimization for materials design with mixed quantitative and qualitative variables
Zhang, Y., Apley, D. W., and Chen, W. (2020) · 2020
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Scalable and flexible deep bayesian optimization with auxiliary information for scientific problems
Kim, S., Lu, P. Y., Loh, C., Smith, J., Snoek, J., and Soljačić, M. (2021) · 2021
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Bayesian optimization with a prior for the optimum
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., and Hutter, F. (2021) · 2021
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π \pi BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Hvarfner, C., Stoll, D., Souza, A., Nardi, L., Lindauer, M., and Hutter, F. (2022) · 2022
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