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Reconciling modern machine-learning practice and the classical bias–variance trade-off
TLDR
This work shows how classical theory and modern practice can be reconciled within a single unified performance curve and proposes a mechanism underlying its emergence, and provides evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets.
To understand deep learning we need to understand kernel learning
TLDR
It is argued that progress on understanding deep learning will be difficult until more tractable "shallow" kernel methods are better understood, and a need for new theoretical ideas for understanding properties of classical kernel methods.
Reconciling modern machine learning and the bias-variance trade-off
TLDR
A new "double descent" risk curve is exhibited that extends the traditional U-shaped bias-variance curve beyond the point of interpolation and shows that the risk of suitably chosen interpolating predictors from these models can, in fact, be decreasing as the model complexity increases, often below the risk achieved using non-interpolating models.
Reply to Loog et al.: Looking beyond the peaking phenomenon
The letter “A brief prehistory of double descent” (1) written in response to our article “Reconciling modern machine-learning practice and the classical bias–variance trade-off” (2) brings a number