Our paper titled “Knowledge Intensive Learning of Credal Networks” has been accepted to the 40th Conference on Uncertainty in Artificial Intelligence (UAI) 2024! This work tackles the problem of parameter learning when the data is sparse, incomplete and uncertain. The authors present an approach based on credal networks, to address this problem of learning the parameters from data using qualitative knowledge in the form of monotonic influence statements. To learn more about the paper, please click here.