68 citations · 213 across the 20 of their papers we have counts for
3 papers · 1 filter
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Ričards Marcinkevičs, Julia E. Vogt
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learn…
Generation of Differentially Private Heterogeneous Electronic Health Records
Kieran Chin-Cheong, Thomas Sutter, Julia E. Vogt
Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advant…
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. However, existi…