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Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty.
Comparison of Approximate Methods for Handling Hyperparameters
D. J. C. Mackay · 1999
Earlier work this paper cites.
Near-optimal Sensor Placements in Gaussian Processes
C. Guestrin, A. Krause, and A. P. Singh · 2005
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
Earlier work this paper cites.
Model-based Geostatistics
P. J. Diggle and P. J. Ribeiro · 2007
Earlier work this paper cites.
An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio · 2007
Earlier work this paper cites.
Hierarchical Gaussian Process Latent Variable Models
N. D. Lawrence and A. J. Moore · 2007
Earlier work this paper cites.
GP-BayesFilters: Bayesian Filtering using Gaussian Process Prediction and Observation Models
J. Ko and D. Fox · 2008
Earlier work this paper cites.
Sequential Bayesian Prediction in the Presence of Changepoints
R. Garnett, M. Osborne, and S. Roberts · 2009
Earlier work this paper cites.
Bayesian Gaussian Process Latent Variable Model
M. K. Titsias and N. D. Lawrence · 2010
Earlier work this paper cites.
Variational Gaussian Process Dynamical Systems
A. C. Damianou, M. K. Titsias, and N. D. Lawrence · 2011
Earlier work this paper cites.
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
M. P. Deisenroth and C. E. Rasmussen · 2011
Earlier work this paper cites.
Robust Multi-class Gaussian Process Classification
D. Hernández-Lobato, H. Lobato, J. Miguel, and P. Dupont · 2011
Earlier work this paper cites.
Two Problems with Variational Expectation Maximisation for Time-Series Models
R. Turner and M. Sahani · 2011
Earlier work this paper cites.
Bayesian Warped Gaussian Processes
M. Lázaro-Gredilla · 2012
Earlier work this paper cites.
Practical Bayesian Optimization of Machine Learning Algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Earlier work this paper cites.
Deep Gaussian Processes
A. C. Damianou and N. D. Lawrence · 2013
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Structure Discovery in Nonparametric Regression through Compositional Kernel Search
D. Duvenaud, J. R. Lloyd, R. Grosse, J. B. Tenenbaum, and Z. Ghahramani · 2013
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Gaussian Processes for Big Data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
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Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
M. K. Titsias and M. Lázaro-Gredilla · 2013
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Searching for Exotic Particles in High-Energy Physics with Deep Learning
P. Baldi, P. Sadowski, and D. Whiteson · 2014
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Avoiding Pathologies in Very Deep Networks
D. Duvenaud, O. Rippel, R. P. Adams, and Z. Ghahramani · 2014
Cited alongside, same era.
Variational Dropout and the Local Reparameterization Trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
Later among the works it cites.
Generic Inference in Latent Gaussian Process Models
E. V. Bonilla, K. Krauth, and A. Dezfouli · 2016
Later among the works it cites.
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
T. D. Bui, D. Hernández-Lobato, Y. Li, J. M. Hernández-Lobato, and R. E. Turner · 2016
Later among the works it cites.
Manifold Gaussian Processes for Regression
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth · 2016
Later among the works it cites.
Variational Auto-encoded Deep Gaussian Processes
Z. Dai, A. Damianou, J. González, and N. Lawrence · 2016
Later among the works it cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
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Nested Variational Compression in Deep Gaussian Processes
J. Hensman and N. D. Lawrence · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, L. Kaiser, M. Kudlur, J. Levenberg, D. Man, R. Monga, S. Moore, D. Murray, J. Shlens, B. Steiner, I. Sutskever, P. Tucker, V. Vanhoucke, V. Vasudevan, O. Vinyals, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2015
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Probabilistic Integration: A Role for Statisticians in Numerical Analysis?
F.-X. Briol, C. J. Oates, M. Girolami, M. A. Osborne, and D. Sejdinovic · 2015
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Efficient Reinforcement Learning for Robots using Informative Simulated Priors
M. Cutler and J. P. How · 2015
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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Y. Gal, Y. Chen, and Z. Ghahramani · 2015
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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
K. Krauth, E. V. Bonilla, K. Cutajar, and M. Filippone · 2016
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On Sparse Variational Methods and The Kullback-Leibler Divergence Between Stochastic Processes
A. G. d. G. Matthews, J. Hensman, R. E. Turner, and Z. Ghahramani · 2016
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Recurrent Gaussian Processes
C. L. C. Mattos, Z. Dai, A. Damianou, J. Forth, G. A. Barreto, and N. D. Lawrence · 2016
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Training Deep Gaussian Processes with Sampling
K. Vafa · 2016
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Sequential Inference for Deep Gaussian Process
Y. Wang, M. Brubaker, B. Chaib-Draa, and R. Urtasun · 2016
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A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing · 2016
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Random Feature Expansions for Deep Gaussian Processes
K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone · 2017
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GPflow: A Gaussian process library using TensorFlow
A. G. Matthews, M. Van Der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahramani, and J. Hensman · 2017
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Asynchronous Distributed Variational Gaussian Processes
H. Peng, S. Zhe, and Y. Qi · 2017
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