5 citations · 7 across the 8 of their papers we have counts for
8 papers
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…
Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net
Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten
The elastic net combines lasso and ridge regression to fuse the sparsity property of lasso with the grouping property of ridge regression. The connections between ridge regression…
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
Oskar Allerbo, Rebecka Jörnsten
High-dimensional data sets are often analyzed and explored via the construction of a latent low-dimensional space which enables convenient visualization and efficient predictive mo…
Flexible, Non-parametric Modeling Using Regularized Neural Networks
Oskar Allerbo, Rebecka Jörnsten
Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often re…