Inference and Learning with Probabilistic Submodular Models

This talk is about a class of probabilistic models defined through
submodular functions, and their use to quantify uncertainty in
submodular optimization. I will present our recent work on approximate
inference and learning with such models, and discuss applications in
computer vision, network analysis and preference elicitation.