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24 papers · 2 filters
Interpolating Conditional Density Trees
Scott Davies, Andrew Moore
Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connec…
Learning with Scope, with Application to Information Extraction and Classification
David Blei, J Andrew Bagnell, Andrew McCallum
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the s…
Learning Riemannian Metrics
Guy Lebanon
We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relat…
Learning using Local Membership Queries
Pranjal Awasthi, Vitaly Feldman, Varun Kanade
We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are \emph{close} to random examples drawn from the under…
A Spectral Algorithm for Latent Junction Trees
Ankur P. Parikh, Le Song, Mariya Ishteva +2
Latent variable models are an elegant framework for capturing rich probabilistic dependencies in many applications. However, current approaches typically parametrize these models u…
A Hierarchical Graphical Model for Record Linkage
Pradeep Ravikumar, William Cohen
The task of matching co-referent records is known among other names as rocord linkage. For large record-linkage problems, often there is little or no labeled data available, but un…