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Neil D. Lawrence | Javier González | Kurt Cutajar | Mark Pullin | Andreas C. Damianou | Neil D. Lawrence | A. Damianou | Kurt Cutajar | Javier I. González | Mark Pullin
[1] Shifeng Xiong,et al. Sequential Design and Analysis of High-Accuracy and Low-Accuracy Computer Codes , 2013, Technometrics.
[2] Slawomir Koziel,et al. Multi-Level CFD-Based Airfoil Shape Optimization With Automated Low-Fidelity Model Selection , 2013, ICCS.
[3] Richard E. Turner,et al. The Gaussian Process Autoregressive Regression Model (GPAR) , 2018, AISTATS.
[4] Kirthevasan Kandasamy,et al. The Multi-fidelity Multi-armed Bandit , 2016, NIPS.
[5] Yuhong Yang,et al. Information Theory, Inference, and Learning Algorithms , 2005 .
[6] Evgeny Burnaev,et al. Large scale variable fidelity surrogate modeling , 2017, Annals of Mathematics and Artificial Intelligence.
[7] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[8] Maurizio Filippone,et al. Random Feature Expansions for Deep Gaussian Processes , 2016, ICML.
[9] Ben Taskar,et al. k-DPPs: Fixed-Size Determinantal Point Processes , 2011, ICML.
[10] Haitao Liu,et al. Cope with diverse data structures in multi-fidelity modeling: A Gaussian process method , 2018, Eng. Appl. Artif. Intell..
[11] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[12] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[13] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[14] O. Macchi. The coincidence approach to stochastic point processes , 1975, Advances in Applied Probability.
[15] Alexis Boukouvalas,et al. GPflow: A Gaussian Process Library using TensorFlow , 2016, J. Mach. Learn. Res..
[16] Ryan P. Adams,et al. Avoiding pathologies in very deep networks , 2014, AISTATS.
[17] Benjamin Peherstorfer,et al. Survey of multifidelity methods in uncertainty propagation, inference, and optimization , 2018, SIAM Rev..
[18] Sonja Kuhnt,et al. Design and analysis of computer experiments , 2010 .
[19] Neil D. Lawrence,et al. Variational Auto-encoded Deep Gaussian Processes , 2015, ICLR.
[20] Marc Peter Deisenroth,et al. Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.
[21] Andrew J Majda,et al. Quantifying uncertainty in climate change science through empirical information theory , 2010, Proceedings of the National Academy of Sciences.
[22] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .
[23] Loic Le Gratiet,et al. RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY , 2012, 1210.0686.
[24] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[25] Andreas C. Damianou,et al. Deep Gaussian processes and variational propagation of uncertainty , 2015 .
[26] Andreas C. Damianou,et al. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[27] George E. Karniadakis,et al. Deep Multi-fidelity Gaussian Processes , 2016, ArXiv.
[28] Carl E. Rasmussen,et al. Manifold Gaussian Processes for regression , 2014, 2016 International Joint Conference on Neural Networks (IJCNN).
[29] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[30] Karen Willcox,et al. Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources , 2015 .
[31] Kirthevasan Kandasamy,et al. Multi-Fidelity Black-Box Optimization with Hierarchical Partitions , 2018, ICML.
[32] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[33] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[34] Jonathan P. How,et al. Reinforcement learning with multi-fidelity simulators , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[35] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.