We propose an algorithm that interleaves the analysis of the decision problem and utility elicitation to allow these two tasks to inform each other.Expand

We examine two representative approaches to explanation in the literature-- Gardenfors and Pearl--and show that both suffer from significant problems.Expand

We argue that the a person's utility value can be treated as we treat other domain attributes: as a random variable with a density function over its possible values.Expand

The Clinical Language Understanding group at Nuance Communications has developed a medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references to patient smoking in clinical reports.Expand

We propose a LinOP-based learning algorithm, inspired by the techniques developed for Bayesian learning, which aggregates the experts' distributions represented as Bayesian networks.Expand

We generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, showing that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.Expand

The majority of real-world probabilistic systems are used by more than one user, thus a utility model must be elicited separately for each new user. Utility elicitation is long and tedious ,… Expand