research
∙
06/29/2023
Solving Kernel Ridge Regression with Gradient-Based Optimization Methods
Kernel ridge regression, KRR, is a non-linear generalization of linear r...
share
research
∙
03/16/2023
Controlled Descent Training
In this work, a novel and model-based artificial neural network (ANN) tr...
research
∙
05/25/2022
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Most complex machine learning and modelling techniques are prone to over...
research
∙
05/24/2022
Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control
Most machine learning methods depend on the tuning of hyper-parameters. ...
research
∙
02/04/2022
Elastic Gradient Descent and Elastic Gradient Flow: LARS Like Algorithms Approximating the Solution Paths of the Elastic Net
The elastic net combines lasso and ridge regression to fuse the sparsity...
research
∙
12/15/2021
Robust Neural Network Classification via Double Regularization
The presence of mislabeled observations in data is a notoriously challen...
research
∙
02/22/2021
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
High-dimensional data sets are often analyzed and explored via the const...
research
∙
12/18/2020
Flexible, Non-parametric Modeling Using Regularized Neural Networks
Non-parametric regression, such as generalized additive models (GAMs), i...
research
∙
11/12/2019
