By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.
Glyman incorporates stakeholders' choices in producing counterfactual explanations to reduce the number of abnormal glycemic events T1D patients encounter.
We introduce a framework for the channel selection problem, mathematically formulate the minimum-cost channel selection (MCCS), and propose two novel algorithms to solve the MCCS problem.