# NIPS 2005 Conference Review Session

Active Learning For Identifying Function Threshold Boundaries
by B. Bryan, J. Schneider, R. Nichol, C. Miller, C. Genovese, L. Wasserman

Brent Bryan

We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the function is above and below a given threshold. We develop experiment selection methods based on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid $1-\alpha$ confidence intervals for seven cosmological parameters. Experimentation shows that the algorithm reduces the computation necessary for the parameter estimation problem by an order of magnitude.

Efficient Value of Information for Graphical Models
by Brigham Anderson and Andrew Moore

Brigham Andersen

Calculations that quantify the dependencies between variables are vital to many operations with graphical models, e.g., active learning and sensitivity analysis. Previously, pairwise information gain calculation has involved a cost quadratic in network size. In this work, we show how to perform a similar computation with cost linear in network size. The loss function that allows this is of a form amenable to computation by dynamic programming. The message-passing algorithm that results is described and empirical results demonstrate large speedups without decrease in accuracy. In the cost-sensitive domains examined, superior accuracy is achieved.

Preconditioner Approximations for Probabilistic Graphical Models
by Pradeep Ravikumar and John Lafferty