130 citations · 671 across the 42 of their papers we have counts for
15 papers · 1 filter
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…
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal in…
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that…