Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders. - Oskar Allerbo; Rebecka Jörnsten (J. Mach. Learn. Res., 2021)

Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control. - Oskar Allerbo; Rebecka Jörnsten (CoRR, 2022)

Flexible, non-parametric modeling using regularized neural networks. - Oskar Allerbo; Rebecka Jörnsten (Comput. Stat., 2022)

Solving Kernel Ridge Regression with Gradient-Based Optimization Methods. - Oskar Allerbo; Rebecka Jörnsten (CoRR, 2023)

Solving Kernel Ridge Regression with Gradient Descent for a Non-Constant Kernel. - Oskar Allerbo (CoRR, 2023)

Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net. - Oskar Allerbo; Johan Jonasson; Rebecka Jörnsten (J. Mach. Learn. Res., 2023)

Efficient training of interpretable, non-linear regression models. - Oskar Allerbo (2023)

Supervised Models Can Generalize Also When Trained on Random Labels. - Oskar Allerbo; Thomas B. Schön (CoRR, 2025)