Search Results for author:
Found 4 papers, 1 papers with code
Targeted Active Learning for Bayesian Decision-Making
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
Active Learning for Decision-Making from Imbalanced Observational Data
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
