Add to ORKGWe study the average CVloo stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm has the best CVloo stability, which in turn is controlled by the condition number of the empirical kernel matrix. The latter can be characterized in the asymptotic regime where both the dimension and cardinality of the data go to infinity. Under the assumption of random kernel matrices, the corresponding test error follows a double descent curve.This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
It is often observed that interpolation based on translates of radial basis functions or non-radial ...
We consider here the class of supervised learning algorithms known as Empirical Risk Minimization (E...
We investigate properties of kernel based regression (KBR) methods which are inspired by the convex ...
© 2017 Neural information processing systems foundation. All rights reserved. Empirical risk minimiz...
Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping ...
© Institute of Mathematical Statistics, 2020. In the absence of explicit regularization, Kernel “Rid...
In the paper "Stability of kernel-based interpolation" (to appear on Adv. Comput. Math.) we prove...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
Kernel approximation is commonly used to scale kernel-based algorithms to applications contain-ing a...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
It is often observed that interpolation based on translates of radial basis functions or non-radial ...
We consider here the class of supervised learning algorithms known as Empirical Risk Minimization (E...
We investigate properties of kernel based regression (KBR) methods which are inspired by the convex ...
© 2017 Neural information processing systems foundation. All rights reserved. Empirical risk minimiz...
Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping ...
© Institute of Mathematical Statistics, 2020. In the absence of explicit regularization, Kernel “Rid...
In the paper "Stability of kernel-based interpolation" (to appear on Adv. Comput. Math.) we prove...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
Kernel approximation is commonly used to scale kernel-based algorithms to applications contain-ing a...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
In several supervised learning applications, it happens that reconstruction methods have to be appli...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
It is often observed that interpolation based on translates of radial basis functions or non-radial ...
We consider here the class of supervised learning algorithms known as Empirical Risk Minimization (E...