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Found 4 papers, 1 papers with code
On the Nystrom Approximation for Preconditioning in Kernel Machines
Scalable algorithms for learning kernel models need to be iterative in nature, but convergence can be slow due to poor conditioning.
On Emergence of Clean-Priority Learning in Early Stopped Neural Networks
This behaviour is believed to be a result of neural networks learning the pattern of clean data first and fitting the noise later in the training, a phenomenon that we refer to as clean-priority learning.
Toward Large Kernel Models
Recent studies indicate that kernel machines can often perform similarly or better than deep neural networks (DNNs) on small datasets.
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
In this work we argue that while benign overfitting has been instructive and fruitful to study, many real interpolating methods like neural networks do not fit benignly: modest noise in the training set causes nonzero (but non-infinite) excess risk at test time, implying these models are neither benign nor catastrophic but rather fall in an intermediate regime.
