Fetching the paper…
Reading the bibliography…
The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis.
H. E. Rauch, F. Tung, and C. T. Striebel, “Maximum likelihood estimates of linear dynamic systems,”
1965
Earlier work this paper cites.
D. Shepard, “A two-dimensional interpolation function for irregularly-spaced data,” in
1968
Earlier work this paper cites.
G. Kimeldorf and G. Wahba, “A correspondence between bayesian estimation of stochastic processes and smoothing by Splines,”
1970
Earlier work this paper cites.
B. W. Silverman, “Some aspects of the Spline Smoothing approach to non-parametric regression curve fitting,”
1985
Earlier work this paper cites.
R. Szeliski, “Regularization uses fractal priors,” in
1987
Earlier work this paper cites.
W. W. Hager, “Updating the inverse of a matrix,”
1989
Earlier work this paper cites.
T. Poggio and F. Girosi, “Networks for approximation and learning,”
1990
Earlier work this paper cites.
G. Wahba,
1990
Earlier work this paper cites.
B. F. Plybon,
1992
Earlier work this paper cites.
D. J. MacKay, “Introduction to Gaussian processes,”
1998
Earlier work this paper cites.
R. L. Burden and J. D. Faires,
2000
Earlier work this paper cites.
E. W. Kamen and B. S. Heck,
2000
Earlier work this paper cites.
J. G. Proakis,
2000
Earlier work this paper cites.
M. E. Tipping, “Sparse Bayesian learning and the Relevance Vector Machine,”
2001
Cited alongside, same era.
B. Schölkopf, A. J. Smola, F. Bach
2002
Cited alongside, same era.
C. E. Rasmussen, “Gaussian processes in machine learning,” in
2003
Cited alongside, same era.
J. Q. Candela, “Learning with uncertainty - Gaussian Processes and Relevance Vector Machines,”
2004
Cited alongside, same era.
C. Robert and G. Casella,
2004
Cited alongside, same era.
C. E. Rasmussen and J. Quiñonero Candela, “Healing the Relevance Vector Machine through augmentation,” in
2005
Cited alongside, same era.
C. M. Bishop,
K. P. Murphy,
2012
Later among the works it cites.
S. Särkkä and J. Hartikainen, “Infinite-dimensional Kalman filtering approach to spatio-temporal Gaussian Process regression,” in
2012
Later among the works it cites.
M. A. Álvarez, L. Rosasco, and N. D. Lawrence, “Kernels for vector-valued functions: A review,”
2012
Later among the works it cites.
S. Särkkä,
2013
Later among the works it cites.
K.-L. Du and M. N. Swamy,
2013
Later among the works it cites.
J. L. Gómez-Dans, P. E. Lewis, and M. Disney, “Efficient emulation of radiative transfer codes using Gaussian processes and application to land surface parameter inferences,”
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2006
Cited alongside, same era.
C. E. Rasmussen, “Gaussian processes for machine learning,” in
2006
Cited alongside, same era.
G. A. Einicke, “Asymptotic optimality of the minimum-variance fixed-interval smoother,”
2007
Cited alongside, same era.
G. A. Einicke, J. C. Ralston, C. O. Hargrave, D. C. Reid, and D. W. Hainsworth, “Longwall mining automation an application of minimum-variance smoothing [applications of control],”
2008
Cited alongside, same era.
M. Alvarez, D. Luengo, and N. D. Lawrence, “Latent force models,” in
2009
Cited alongside, same era.
J. Hartikainen and S. Särkkä, “Kalman filtering and smoothing solutions to temporal Gaussian process regression models,” in
2010
Cited alongside, same era.
D. H. Svendsen, L. Martino, M. Campos-Taberner, F. J. García-Haro, and G. Camps-Valls, “Joint Gaussian processes for biophysical parameter retrieval,”
2017
Later among the works it cites.
L. Martino, J. Vicent, and G. Camps-Valls, “Automatic emulation by adaptive Relevance Vector Machines,”
2017
Later among the works it cites.
L. Martino, D. Luengo, and J. Miguez,
2018
Later among the works it cites.
U. B. Gewali, S. T. Monteiro, and E. Saber, “Gaussian Processes for vegetation parameter estimation from hyperspectral data with limited ground truth,”
2019
Later among the works it cites.
2019
Later among the works it cites.
D. H. Svendsen, L. Martino, and G. Camps-Valls, “Active emulation of computer codes with Gaussian processes–application to remote sensing,”
2020
Closest in time.
J. Read and L. Martino, “Probabilistic regressor chains with Monte Carlo methods,”
2020
Closest in time.