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Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.
The coincidence approach to stochastic point processes
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Predicting the output from a complex computer code when fast approximations are available
Kennedy, M. C. and O’Hagan, A · 2000
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Information theory, inference and learning algorithms , chapter 28, pp. 343–355
MacKay, D. J · 2003
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The design and analysis of computer experiments
Morris, M. D · 2004
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Rasmussen, C. E. and Williams, C · 2006
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Quantifying uncertainty in climate change science through empirical information theory
Majda, A. J. and Gershgorin, B · 2010
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k-DPPs: Fixed-size determinantal point processes
Kulesza, A. and Taskar, B · 2011
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Deep Gaussian processes
Damianou, A. and Lawrence, N. D · 2013
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Gaussian processes for big data
Hensman, J., Fusi, N., and Lawrence, N. D · 2013
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. W · 2013
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Multi-level CFD-based airfoil shape optimization with automated low-fidelity model selection
Koziel, S. and Leifsson, L · 2013
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Reinforcement learning with multi-fidelity simulators
Cutler, M., Walsh, T. J., and How, J. P · 2014
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Avoiding pathologies in very deep networks
Duvenaud, D. K., Rippel, O., Adams, R. P., and Ghahramani, Z · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Recursive co-kriging model for design of computer experiments with multiple levels of fidelity
Le Gratiet, L. and Garnier, J · 2014
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Deep Gaussian processes and variational propagation of uncertainty
Damianou, A · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 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
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Performance study of gradient-enhanced kriging
Ulaganathan, S., Couckuyt, I., Dhaene, T., Degroote, J., and Laermans, E · 2016
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Random feature expansions for deep Gaussian processes
Cutajar, K., Bonilla, E. V., Michiardi, P., and Filippone, M · 2017
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GPflow: A Gaussian process library using TensorFlow
Matthews, A. G. d. G., van der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z., and Hensman, J · 2017
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
Perdikaris, P., Raissi, M., Damianou, A., Lawrence, N. D., and Karniadakis, G. E · 2017
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Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P. I · 2017
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Doubly stochastic variational inference for deep Gaussian processes
Salimbeni, H. and Deisenroth, M. P · 2017
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Manifold Gaussian processes for regression
Calandra, R., Peters, J., Rasmussen, C. E., and Deisenroth, M. P · 2016
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Variational auto-encoded deep Gaussian processes
Dai, Z., Damianou, A., González, J., and Lawrence, N · 2016
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The multi-fidelity multi-armed bandit
Kandasamy, K., Dasarathy, G., Póczos, B., and Schneider, J. G · 2016
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Deep multi-fidelity Gaussian processes
Raissi, M. and Karniadakis, G · 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
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Large scale variable fidelity surrogate modeling
Zaytsev, A. and Burnaev, E · 2017
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Cope with diverse data structures in multi-fidelity modeling: A Gaussian process method
Liu, H., Ong, Y., Cai, J., and Wang, Y · 2018
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Survey of multifidelity methods in uncertainty propagation, inference, and optimization
Peherstorfer, B., Willcox, K., and Gunzburger, M · 2018
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Multi-fidelity black-box optimization with hierarchical partitions
Sen, R., Kandasamy, K., and Shakkottai, S · 2018
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The Gaussian process autoregressive regression model (GPAR)
Requeima, J., Tebbutt, W., Bruinsma, W., and Turner, R. E · 2019
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