New conditions for support vector proliferation (SVP) in RKHS for bounded orthonormal systems and sub-Gaussian features, yielding generalization bounds for kernel SVMs beyond prior restrictive assumptions.
A Fa rewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
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Simulations show that least-squares interpolation on contaminated data exhibits double descent with superior generalization over robust alternatives at high overparameterization.
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New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
New conditions for support vector proliferation (SVP) in RKHS for bounded orthonormal systems and sub-Gaussian features, yielding generalization bounds for kernel SVMs beyond prior restrictive assumptions.
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Double descent for least-squares interpolation on contaminated data: A simulation study
Simulations show that least-squares interpolation on contaminated data exhibits double descent with superior generalization over robust alternatives at high overparameterization.