Section: New Results
Structured Learning and Inference
Paticipants: Jiaqian Yu, Matthew Blaschko
We have developed computationally efficient structured output prediction methods for learning with non-modular lossesĀ [19] , [29] , [40] . We both demonstrate the feasibility of learning with submodular losses, as well as show that learning with multiple correct outputs can lead to NP-hard optimization problems even when learning with a single correct output is feasible.