Representer theorems

September 16, 2019 — September 16, 2019

functional analysis
Hilbert space
kernel tricks

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


Figure 1

1 References

Bohn, Griebel, and Rieger. 2018. A Representer Theorem for Deep Kernel Learning.” arXiv:1709.10441 [Cs, Math].
Boyer, Chambolle, De Castro, et al. 2018. On Representer Theorems and Convex Regularization.” arXiv:1806.09810 [Cs, Math].
Boyer, Chambolle, de Castro, et al. 2018. Convex Regularization and Representer Theorems.” In arXiv:1812.04355 [Cs, Math].
Chernozhukov, Newey, and Singh. 2018. Learning L2 Continuous Regression Functionals via Regularized Riesz Representers.” arXiv:1809.05224 [Econ, Math, Stat].
Kar, and Karnick. 2012. Random Feature Maps for Dot Product Kernels.” In Artificial Intelligence and Statistics.
Kimeldorf, and Wahba. 1970. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines.” The Annals of Mathematical Statistics.
Schlegel. 2018. When Is There a Representer Theorem? Reflexive Banach Spaces.” arXiv:1809.10284 [Cs, Math, Stat].
Schölkopf, Herbrich, and Smola. 2001. A Generalized Representer Theorem.” In Computational Learning Theory. Lecture Notes in Computer Science.
Unser. 2019. A Representer Theorem for Deep Neural Networks.” Journal of Machine Learning Research.
Walder, Schölkopf, and Chapelle. 2006. Implicit Surface Modelling with a Globally Regularised Basis of Compact Support.” Computer Graphics Forum.
Yu, Cheng, Schuurmans, et al. 2013. Characterizing the Representer Theorem.” In Proceedings of the 30th International Conference on Machine Learning (ICML-13).