Representer theorems



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

🏗

References

Bohn, Bastian, Michael Griebel, and Christian Rieger. 2018. A Representer Theorem for Deep Kernel Learning.” arXiv:1709.10441 [Cs, Math], June.
Boyer, Claire, Antonin Chambolle, Yohann de Castro, Vincent Duval, Frédéric de Gournay, and Pierre Weiss. 2018. Convex Regularization and Representer Theorems.” In arXiv:1812.04355 [Cs, Math].
Boyer, Claire, Antonin Chambolle, Yohann De Castro, Vincent Duval, Frédéric De Gournay, and Pierre Weiss. 2018. On Representer Theorems and Convex Regularization.” arXiv:1806.09810 [Cs, Math], June.
Chernozhukov, Victor, Whitney K. Newey, and Rahul Singh. 2018. Learning L2 Continuous Regression Functionals via Regularized Riesz Representers.” arXiv:1809.05224 [Econ, Math, Stat], September.
Kar, Purushottam, and Harish Karnick. 2012. Random Feature Maps for Dot Product Kernels.” In Artificial Intelligence and Statistics, 583–91. PMLR.
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.” arXiv:1809.10284 [Cs, Math, Stat], 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.
Unser, Michael. 2019. A Representer Theorem for Deep Neural Networks.” Journal of Machine Learning Research 20 (110): 30.
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.

No comments yet. Why not leave one?

GitHub-flavored Markdown & a sane subset of HTML is supported.