Representer theorems

In spatial statistics, Gaussian processes, kernel machines and covariance functions, regularisation.


Bohn, Bastian, Michael Griebel, and Christian Rieger. 2018. “A Representer Theorem for Deep Kernel Learning,” June.

Boyer, Claire, Antonin Chambolle, Yohann de Castro, Vincent Duval, Frédéric de Gournay, and Pierre Weiss. 2018. “Convex Regularization and Representer Theorems.” In.

Boyer, Claire, Antonin Chambolle, Yohann De Castro, Vincent Duval, Frédéric De Gournay, and Pierre Weiss. 2018. “On Representer Theorems and Convex Regularization,” June.

Chernozhukov, Victor, Whitney K. Newey, and Rahul Singh. 2018. “Learning L2 Continuous Regression Functionals via Regularized Riesz Representers,” September.

Kimeldorf, George S., and Grace Wahba. 1970. “A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines.” The Annals of Mathematical Statistics 41 (2): 495–502.

Schlegel, Kevin. 2018. “When Is There a Representer Theorem? Reflexive Banach Spaces,” September.

Schölkopf, Bernhard, Ralf Herbrich, and Alex J. Smola. 2001. “A Generalized Representer Theorem.” In Computational Learning Theory, edited by David Helmbold and Bob Williamson, 416–26. Lecture Notes in Computer Science. Springer Berlin Heidelberg.

Walder, C., B. Schölkopf, and O. Chapelle. 2006. “Implicit Surface Modelling with a Globally Regularised Basis of Compact Support.” Computer Graphics Forum 25 (3): 635–44.

Yu, Yaoliang, Hao Cheng, Dale Schuurmans, and Csaba Szepesvári. 2013. “Characterizing the Representer Theorem.” In Proceedings of the 30th International Conference on Machine Learning (ICML-13), 570–78.