6 papers
Evaluating Explanation Methods by the Predictors They Induce
Jacob Selbæk, Hugo L. Hammer
Explanations of machine learning models are usually judged by criteria that are hard to compare. We propose a simpler test: if an explanation really describes how a model uses its…
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton +1
Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.…
Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation
Adam Jakobsen, Sushant Gautam, Hugo Lewi Hammer +4
AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To…
Using Large Language Models to Suggest Informative Prior Distributions in Bayesian Statistics
Michael A. Riegler, Kristoffer Herland Hellton, Vajira Thambawita +1
Selecting prior distributions in Bayesian statistics is challenging, resource-intensive, and subjective. We analyze using large-language models (LLMs) to suggest suitable, knowledg…
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…
Principal Component Analysis based frameworks for efficient missing data imputation algorithms
Thu Nguyen, Hoang Thien Ly, Michael Alexander Riegler +2
Missing data is a commonly occurring problem in practice. Many imputation methods have been developed to fill in the missing entries. However, not all of them can scale to high-dim…