• Publications
  • Influence
Making Rational Decisions Using Adaptive Utility Elicitation
TLDR
An algorithm is proposed that interleaves the analysis of the decision problem and utility elicitation to allow these two tasks to inform each other and computes the best strategy based on the information acquired so far.
Defining Explanation in Probabilistic Systems
TLDR
An approach to defining a notion of "better explanation" is proposed that combines some of the features of both Gardenfors and Pearl together with more recent work by Pearl and others on causality.
Utilities as Random Variables: Density Estimation and Structure Discovery
TLDR
It is argued that the a person's utility value for a given outcome can be treated as the authors treat other domain attributes: as a random variable with a density function over its possible values.
Case Report: Identifying Smokers with a Medical Extraction System
TLDR
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 and shows overall accuracy in the 90s on all data sets used.
Utility Elicitation as a Classification Problem
TLDR
This work attempts to identify the new user's utility function based on classification relative to a database of previously collected utility functions by identifying clusters of utility functions that minimize an appropriate distance measure.
Explaining Predictions in Bayesian Networks and Influence Diagrams
TLDR
This work focuses on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems, and analyzes the issues involved in defining, computing and evaluating such explanations.
Aggregating Learned Probabilistic Beliefs
TLDR
A LinOP-based learning algorithm is proposed, inspired by the techniques developed for Bayesian learning, which aggregates the experts' distributions represented as Bayesian networks.
Axiomatic Interpretability for Multiclass Additive Models
TLDR
A state-of-the-art GAM learning algorithm based on boosted trees is generalized 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.
Utility Elicitation as a Classiication Problem
TLDR
This work attempts to cluster a database of existing user utility functions into a small number of prototypical utility functions, so that a new user's utility function can be classified by asking many fewer and simpler assessments than full utility model elicitation would require.
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