Microsoft Research
UAI '94: Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence, 1994 – UAI
UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, 1993 – UAI
UAI '86: Proceedings of the Second Annual Conference on Uncertainty in Artificial Intelligence, 1986 – UAI
UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995 – UAI
Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal ...
Ronen I. Brafman, David Heckerman, Guy Shani
Proceedings of the Thirteenth International Conference on Automated Planning and Scheduling (ICAPS 2003), 2003 – ICAPS
Recommender systems -- systems that suggest to users in e-commerce sites items that might interest them -- adopt a static view of the recommendation process and treat it as a prediction problem. In an earlier paper, we argued that it is more ...
UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995 – UAI
Bo Thiesson, Christopher Meek, David Heckerman
Machine Learning, vol. 45,no. 3,2001 – ML
The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires signi cant computational resources and has been dismissed as impractical for large databases. We ...
Francis R. Bach, David Heckerman, Eric Horvitz
Journal of Machine Learning Research, vol. 7,2006 – JMLR
Receiver Operating Characteristic (ROC) curves are a standard way to display the performance of a set of binary classifiers for all feasible ratios of the costs associated with false positives and false negatives. For linear classifiers, the set of ...