60 citations · 67 across the 5 of their papers we have counts for
5 papers
Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss
Laurens Sluijterman, Frank Kreuwel, Eric Cator +1
This paper explores the use of XGBoost for composite quantile regression. XGBoost is a highly popular model renowned for its flexibility, efficiency, and capability to deal with mi…
Likelihood-ratio-based confidence intervals for neural networks
Laurens Sluijterman, Eric Cator, Tom Heskes
This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks. Our method, called DeepLR, offers…
Optimal Training of Mean Variance Estimation Neural Networks
Laurens Sluijterman, Eric Cator, Tom Heskes
This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building bloc…
Confident Neural Network Regression with Bootstrapped Deep Ensembles
Laurens Sluijterman, Eric Cator, Tom Heskes
With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimatio…
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
Laurens Sluijterman, Eric Cator, Tom Heskes
As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. M…