Active Learning For Identifying Function Threshold Boundaries
by B. Bryan, J. Schneider, R. Nichol, C. Miller, C. Genovese, L. Wasserman
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
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
We present a family of approximation techniques for probabilistic graphical models, based on the use of graphical preconditioners developed in the scientific computing literature. Our framework yields rigorous upper and lower bounds on event probabilities and the log partition function of undirected graphical models, using non-iterative procedures that have low time complexity. As in mean field approaches, the approximations are built upon tractable subgraphs; however, we recast the problem of optimizing the tractable distribution parameters and approximate inference in terms of the well-studied linear systems problem of obtaining a good matrix preconditioner. Experiments are presented that compare the new approximation schemes to variational methods.
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Pradeep Ravikumar Last modified: Sun Jan 29 10:43:35 EST 2006