828 citations · 5.6k across the 171 of their papers we have counts for
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A New Class of Upper Bounds on the Log Partition Function
Martin Wainwright, Tommi S. Jaakkola, Alan Willsky
Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis. We intr…
Unsupervised Active Learning in Large Domains
Harald Steck, Tommi S. Jaakkola
Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to whi…
Continuation Methods for Mixing Heterogenous Sources
Adrian Corduneanu, Tommi S. Jaakkola
A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems…
On Information Regularization
Adrian Corduneanu, Tommi S. Jaakkola
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style…
Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005)
Fahiem Bacchus, Tommi Jaakkola
This is the Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29 2005.
On the Partition Function and Random Maximum A-Posteriori Perturbations
Tamir Hazan, Tommi Jaakkola
In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition f…