Search Results for author:
Found 7 papers, 2 papers with code
dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data
We then propose data-compatible FCI (dcFCI), the first hybrid causal discovery algorithm to jointly address latent confounding, empirical unfaithfulness, and mixed data types.
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD.
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.
MOSGA: Modular Open-Source Genome Annotator
The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies.
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Artificial intelligence (AI) has been successfully applied in numerous scientific domains.
The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes
However, currently available AI systems do not interact with a patient, e. g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis.
FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects.
