KIP
Kernel Inducing Points
**Kernel Inducing Points**, or **KIP**, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn $\epsilon$-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.
Dataset Meta-Learning from Kernel Ridge-Regression
https://paperswithcode.com/paper/dataset-meta-learning-from-kernel-ridge-1
Dataset Meta-Learning from Kernel Ridge-Regression