Representer theorems

September 16, 2019 — September 16, 2019

approximation
functional analysis
Hilbert space
kernel tricks
metrics
statistics

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

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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).