Just Interpolate: Kernel "Ridgeless" Regression Can Generalize

Generalized cross-validation as a method for choosing a good ridge parameter

Gene H. Golub, Michael Heath, and Grace Wahba · 1979

Earlier work this paper cites.

On the limit of the largest eigenvalue of the large dimensional sample covariance matrix

Yong-Quan Yin, Zhi-Dong Bai, and Pathak R Krishnaiah · 1988

Earlier work this paper cites.

The origins of kriging

Noel Cressie · 1990

Earlier work this paper cites.

Spline models for observational data , volume 59

Grace Wahba · 1990

Earlier work this paper cites.

Learning with kernels , volume 4

Alex J Smola and Bernhard Schölkopf · 1998

Earlier work this paper cites.

Statistical learning theory. 1998 , volume 3

Vladimir Vapnik · 1998

Earlier work this paper cites.

Regularization networks and support vector machines

Theodoros Evgeniou, Massimiliano Pontil, and Tomaso Poggio · 2000

Earlier work this paper cites.

Rademacher and gaussian complexities: Risk bounds and structural results

Peter L Bartlett and Shahar Mendelson · 2002

Earlier work this paper cites.

Best choices for regularization parameters in learning theory: on the bias-variance problem

Felipe Cucker and Steve Smale · 2002

Earlier work this paper cites.

Limiting spectral distribution of large dimensional random matrices

Arup Bose, Sourav Chatterjee, and Sreela Gangopadhyay · 2003

Earlier work this paper cites.

A few notes on statistical learning theory

Shahar Mendelson · 2003

Cited alongside, same era.

Kernel methods for pattern analysis

John Shawe-Taylor and Nello Cristianini · 2004

Cited alongside, same era.

Model selection for regularized least-squares algorithm in learning theory

Ernesto De Vito, Andrea Caponnetto, and Lorenzo Rosasco · 2005

Cited alongside, same era.

Exponential convergence rates in classification

Vladimir Koltchinskii and Olexandra Beznosova · 2005

Cited alongside, same era.

A distribution-free theory of nonparametric regression

László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2006

Cited alongside, same era.

Optimal rates for the regularized least-squares algorithm

Andrea Caponnetto and Ernesto De Vito · 2007

Cited alongside, same era.

Kernels for vector-valued functions: A review

Mauricio A Alvarez, Lorenzo Rosasco, and Neil D Lawrence · 2012

Later among the works it cites.

Concentration inequalities: A nonasymptotic theory of independence

Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013

Later among the works it cites.

Kernel ridge regression

Vladimir Vovk · 2013

Later among the works it cites.

In search of the real inductive bias: On the role of implicit regularization in deep learning

Original

Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014

Later among the works it cites.

Understanding deep learning requires rethinking generalization

Original

Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016

Later among the works it cites.