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New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick.
Harmless interpolation in regression and classification with structured features
Our results recover prior independent-features results (with a much simpler analysis), but they furthermore show that harmless interpolation can occur in more general settings such as features that are a bounded orthonormal system.
Low-rank matrix completion and denoising under Poisson noise
This paper considers the problem of estimating a low-rank matrix from the observation of all or a subset of its entries in the presence of Poisson noise.
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