Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle, Anton Matsson, Newton Mwai +1
Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often ad…
cs.LG2024
How Should We Represent History in Interpretable Models of Clinical Policies?
Anton Matsson, Lena Stempfle, Yaochen Rao +3
Modeling policies for sequential clinical decision-making based on observational data is useful for describing treatment practices, standardizing frequent patterns in treatment, an…
cs.LG2024
Unsupervised domain adaptation by learning using privileged information
Adam Breitholtz, Anton Matsson, Fredrik D. Johansson
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in hig…