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Inference papers
- M. Wainwright, T. Jaakkola, and A. Willsky.
Map estimation via agreement on (hyper)trees: Message-passing and
linear-programming approaches.
IEEE Transactions on Information Theory, 51(11):3697--3717,
2005.
[pdf]
- M. Wainwright, T. Jaakkola, and A. Willsky.
A new class of upper bounds on the log partition function.
IEEE Transactions on Information Theory, 51:2313--2335, 2005.
[pdf]
- M. Wainwright, T. Jaakkola, and A. Willsky.
Tree consistency and bounds on the performance of the max-product
algorithm and its generalizations.
Statistics and Computing, 14(2):143--166, 2004.
[pdf]
- M. Wainwright, T. Jaakkola, and A. Willsky.
Tree-based parameterization framework for analysis of belief
propagation and related algorithms.
IEEE Transactions on information theory, 2002.
- M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
Exact map estimates by (hyper)tree agreement.
In Advances in Neural Information processing systems 15, 2002.
[gzipped postscript]
- M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
A new class of upper bounds on the log partition function.
In Proceedings of the Eighteenth Annual Conference on
Uncertainty in Artificial Intelligence, 2002.
[gzipped postscript]
- M. Wainwright, T. Jaakkola, and A. Willsky.
Tree-based reparameterization for approximate estimation on loopy
graphs.
In Advances in Neural Information processing systems 14, 2001.
[pdf]
- M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
Tree-based reparameterization framework for approximate estimation in
graphs with cycles.
LIDS Technical Report P-2510, 2001.
[gzipped postscript]
- T. Jaakkola.
Tutorial on variational approximation methods.
In Advanced mean field methods: theory and practice. MIT Press,
2000.
[postscript], [gzipped postscript]
- B. Frey, R. Patrascu, T. Jaakkola, and J. Moran.
Sequentially fitting inclusive trees for inference in noisy-or
networks.
In Advances in Neural Information Processing Systems 13. MIT
Press, 2000.
[postscript], [gzipped postscript]
- T. Jaakkola and M. Jordan.
Variational probabilistic inference and the qmr-dt database.
Journal of Artificial Intelligence Research, 10:291--322, 1999.
[postscript], [gzipped postscript], [pdf]
- M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
An introduction to variational methods for graphical models.
Machine Learning, 37(2):183, 1999.
[postscript], [gzipped postscript]
- C. Bishop, N. Lawrence, T. Jaakkola, and M. Jordan.
Approximating posterior distributions in belief networks using
mixtures.
In Advances in Neural Information Processing Systems 10, 1997.
[postscript], [gzipped postscript]
- T. Jaakkola.
Variational methods for inference and estimation in graphical
models.
PhD thesis, MIT, 1997.
[postscript], [gzipped postscript]
- T. Jaakkola and M. Jordan.
Improving the mean field approximation via the use of mixture
distributions.
In Proceedings of the NATO ASI on Learning in Graphical Models.
Kluwer, 1997.
[postscript], [gzipped postscript]
- L. Saul, T. Jaakkola, and M. Jordan.
Mean field theory for sigmoid belief networks.
Journal of Artificial Intelligence Research, 4:61--76, 1996.
[postscript], [gzipped postscript], [pdf]
- T. Jaakkola and M. Jordan.
Recursive algorithms for approximating probabilities in graphical
models.
In Advances in Neural Information Processing Systems 9, 1996.
[postscript], [gzipped postscript]
- T. Jaakkola and M. Jordan.
Computing upper and lower bounds on likelihoods in intractable
networks.
In Proceedings of the Twelfth Annual Conference on Uncertainty
in Artificial Intelligence, pages 340--348, 1996.
[postscript], [gzipped postscript]
- T. Jaakkola, L. Saul, and M. Jordan.
Fast learning by bounding likelihoods in sigmoid type belief
networks.
In Advances in Neural Information Processing Systems 8, 1995.
[postscript], [gzipped postscript]
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