Fetching the paper…
Reading the bibliography…
The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art.
Tauberian theorems
Wiener, N · 1932
Earlier work this paper cites.
Lectures on Fourier Integrals: With an Author’s Supplement on Monotonic Functions, Stieltjes Integrals and Harmonic Analysis; Translated from the Original German by Morris Tenenbaum and Harry Pollard
Bochner, S · 1959
Earlier work this paper cites.
Estimating the dimension of a model
Schwarz, G. et al · 1978
Earlier work this paper cites.
A note on harmonizable and v-bounded processes
Kakihara, Y · 1985
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D · 1989
Earlier work this paper cites.
Evaluation of Gaussian processes and other methods for non-linear regression
Rasmussen, C. E · 1999
Earlier work this paper cites.
Classes of kernels for machine learning: a statistics perspective
Genton, M. G · 2001
Earlier work this paper cites.
Correlation theory of stationary and related random functions: Supplementary notes and references
Yaglom, A. M · 2001
Earlier work this paper cites.
UCI machine learning repository, 2007
Asuncion, A. and Newman, D · 2007
Earlier work this paper cites.
Using deep belief nets to learn covariance kernels for Gaussian processes
Hinton, G. E. and Salakhutdinov, R. R · 2008
Earlier work this paper cites.
Deep, narrow sigmoid belief networks are universal approximators
Sutskever, I. and Hinton, G. E · 2008
Earlier work this paper cites.
Exploring large feature spaces with hierarchical multiple kernel learning
Bach, F. R · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse Gaussian processes
Titsias, M · 2009
Cited alongside, same era.
Brochu, E., Cora, V. M., and De Freitas, N · 2010
Cited alongside, same era.
Sparse spectrum Gaussian process regression
Lázaro-Gredilla, M., Quiñonero Candela, J., Rasmussen, C. E., and Figueiras-Vidal, A. R · 2010
Cited alongside, same era.
Information theory: coding theorems for discrete memoryless systems
Csiszar, I. and Körner, J · 2011
Cited alongside, same era.
Additive Gaussian processes
Duvenaud, D. K., Nickisch, H., and Rasmussen, C. E · 2011
Cited alongside, same era.
Sum-product networks: A new deep architecture
Poon, H. and Domingos, P · 2011
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Later among the works it cites.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
Later among the works it cites.
High dimensional bayesian optimisation and bandits via additive models
Kandasamy, K., Schneider, J., and Póczos, B · 2015
Later among the works it cites.
Kom Samo, Y.-L. and Roberts, S · 2015
Later among the works it cites.
Learning stationary time series using gaussian processes with nonparametric kernels
Tobar, F., Bui, T. D., and Turner, R. E · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Scalable inference for structured Gaussian process models
Saatçi, Y · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
Cited alongside, same era.
Structure discovery in nonparametric regression through compositional kernel search
Duvenaud, D., Lloyd, J. R., Grosse, R., Tenenbaum, J. B., and Ghahramani, Z · 2013
Cited alongside, same era.
Gaussian process kernels for pattern discovery and extrapolation
Wilson, A. and Adams, R · 2013
Cited alongside, same era.
Automatic construction and natural-language description of nonparametric regression models
Lloyd, J. R., Duvenaud, D. K., Grosse, R. B., Tenenbaum, J. B., and Ghahramani, Z · 2014
Cited alongside, same era.
Fast kernel learning for multidimensional pattern extrapolation
Wilson, A. G., Gilboa, E., Nehorai, A., and Cunningham, J. P · 2014
Cited alongside, same era.
Bayesian optimization for automated model selection
Malkomes, G., Schaff, C., and Garnett, R · 2016
Later among the works it cites.
Bayesian nonparametric kernel-learning
Oliva, J. B., Dubey, A., Wilson, A. G., Póczos, B., Schneider, J., and Xing, E. P · 2016
Later among the works it cites.
Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
Later among the works it cites.
Discovering and exploiting additive structure for Bayesian optimization
Gardner, J., Guo, C., Weinberger, K., Garnett, R., and Grosse, R · 2017
Later among the works it cites.
Non-stationary spectral kernels
Remes, S., Heinonen, M., and Kaski, S · 2017
Later among the works it cites.
Batched high-dimensional bayesian optimization via structural kernel learning
Wang, Z., Li, C., Jegelka, S., and Kohli, P · 2017
Later among the works it cites.