A la Carte - Learning Fast Kernels

Functions of positive and negative type and their connection with the theory of integral equations

J. Mercer · 1909

Earlier work this paper cites.

Positive definite functions on spheres

I. Schoenberg · 1942

Earlier work this paper cites.

A correspondence between Bayesian estimation on stochastic processes and smoothing by splines

G. S. Kimeldorf and G. Wahba · 1970

Earlier work this paper cites.

Density Estimation for Statistical and Data Analysis

B. W. Silverman · 1986

Earlier work this paper cites.

Assessing relevance determination methods using delve

R. M. Neal · 1998

Earlier work this paper cites.

Prediction with Gaussian processes: From linear regression to linear prediction and beyond

C. K. I. Williams · 1998

Earlier work this paper cites.

Sequential Monte Carlo Methods in Practice

A. Doucet, N. de Freitas, and N. Gordon · 2001

Earlier work this paper cites.

A generalized representer theorem

B. Schölkopf, R. Herbrich, and A. J. Smola · 2001

Cited alongside, same era.

Hyperkernels

C. S. Ong, A. J. Smola, and R. C. Williamson · 2003

Cited alongside, same era.

Gaussian Processes for Machine Learning

C. E. Rasmussen and C. K. I. Williams · 2006

Cited alongside, same era.

Random features for large-scale kernel machines

A. Rahimi and B. Recht · 2008

Cited alongside, same era.

Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions, 2009

Original

N. Halko, P.G. Martinsson, and J. A. Tropp · 2009

Cited alongside, same era.

Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning

A. Rahimi and B. Recht · 2009

Cited alongside, same era.

Sparse spectrum gaussian process regression

M. Lázaro-Gredilla, J. Quiñonero-Candela, C. E. Rasmussen, and A. R. Figueiras-Vidal · 2010

Later among the works it cites.

Fastfood — computing hilbert space expansions in loglinear time

Q.V. Le, T. Sarlos, and A. J. Smola · 2013

Later among the works it cites.

Gaussian process kernels for pattern discovery and extrapolation

A. G. Wilson and R. P. Adams · 2013

Later among the works it cites.

How to scale up kernel methods to be as good as deep neural nets

Original

Z. Lu, M. May, K. Liu, A.B. Garakani, Guo D., A. Bellet, L. Fan, M. Collins, B. Kingsbury, M. Picheny, and F. Sha · 2014

Closest in time.

Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes

A.G. Wilson · 2014

Closest in time.

Fast kernel learning for multidimensional pattern extrapolation