5 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
A Rigorous, Tractable Measure of Model Complexity
Oskar Allerbo, Thomas B. Schön
An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existing complexity measures either…
Is Supervised Learning Really That Different from Unsupervised?
Oskar Allerbo, Thomas B. Schön
We demonstrate how supervised learning can be decomposed into a two-stage procedure, where (1) all model parameters are selected in an unsupervised manner, and (2) the outputs y ar…
Changing the Kernel During Training Leads to Double Descent in Kernel Regression
Oskar Allerbo
We investigate changing the bandwidth of a translational-invariant kernel during training when solving kernel regression with gradient descent. We present a theoretical bound on th…
Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
Oskar Allerbo
Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the model parameters. Here, we introduce an equivalent fo…
Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control
Oskar Allerbo, Rebecka Jörnsten
Most machine learning methods require tuning of hyper-parameters. For kernel ridge regression with the Gaussian kernel, the hyper-parameter is the bandwidth. The bandwidth specifie…