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Machine learning papers
- D. Sontag and T. Jaakkola.
Tree block coordinate descent for map in graphical models.
In Proceedings of the 12th International Conference on
Artificial Intelligence and Statistics, 2009.
To appear.
[pdf]
- D. Sontag, A. Globerson, and T. Jaakkola.
Clusters and coarse partitions in lp relaxations.
In Advances in Neural Information Processing Systems 21, 2008.
[pdf]
- D. Sontag, T. Meltzer, A. Globerson, T. Jaakkola, and Y. Weiss.
Tightening lp relaxations for map using message passing.
In Proceedings of the 24rd Conference on Uncertainty in
Artificial Intelligence, 2008.
[pdf]
- D. Sontag and T. Jaakkola.
New outer bounds on the marginal polytope.
In Advances in Neural Information Processing Systems 20, 2007.
[pdf]
- A. Globerson and T. Jaakkola.
Fixing max-product: Convergent message passing algorithms for map
lp-relaxations.
In Advances in Neural Information Processing Systems 20, 2007.
[pdf]
- A. Globerson and T. Jaakkola.
Convergent propagation algorithms via oriented trees.
In Proceedings of the 23rd Conference on Uncertainty in
Artificial Intelligence, 2007.
[pdf]
- D. Sontag and T. Jaakkola.
On iteratively constraining the marginal polytope for approximate
inference and map.
Technical report, 2007.
[pdf]
- A. Globerson and T. Jaakkola.
Approximate inference using conditional entropy decompositions.
In Proceedings of the 11th International Conference on
Artificial Intelligence and Statistics, 2007.
[pdf]
- H. Steck and T. Jaakkola.
Predictive discretization during model selection.
In Proceedings of the 11th International Conference on
Artificial Intelligence and Statistics, 2007.
[pdf]
- A. Globerson and T. Jaakkola.
Approximate inference using planar graph decomposition.
In Advances in Neural Information Processing Systems 19, 2006.
[pdf]
- L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
Game theoretic algorithms for protein-dna binding.
In Advances in Neural Information Processing Systems 19, 2006.
[pdf]
- A. Qi and T. Jaakkola.
Parameter expanded variational bayesian methods.
In Advances in Neural Information Processing Systems 19, 2006.
[pdf]
- A. Corduneanu and T. Jaakkola.
Data dependent regularization.
In Semi-supervised learning. MIT Press, 2006.
[pdf]
- 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]
- J. Rennie and T. Jaakkola.
Using term informativeness for named entity detection.
In Proceedings of the 28th Annual Conference on Research and
Development in Information Retrieval (SIGIR), 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]
- R. Rosales and T. Jaakkola.
Focused inference.
In Proceedings of the Tenth International Workshop on Artificial
Intelligence and Statistics, 2005.
[pdf]
- N. Srebro, N. Alon, and T. Jaakkola.
Generalization error bounds for collaborative prediction with
low-rank matrices.
In Advances in Neural Information Processing Systems 17, 2004.
[pdf]
- N. Srebro, J. Rennie, and T. Jaakkola.
Maximum margin matrix factorization.
In Advances in Neural Information Processing Systems 17, 2004.
[pdf]
- A. Corduneanu and T. Jaakkola.
Distributed information regularization on graphs.
In Advances in Neural Information Processing Systems 17, 2004.
[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]
- N. Srebro and T. Jaakkola.
Linear dependent dimensionality reduction.
In Advances in Neural Information Processing Systems 16, 2003.
[pdf]
- C. Monteleoni and T. Jaakkola.
Online learning of non-stationary sequences.
In Advances in Neural Information Processing Systems 16, 2003.
[gzipped postscript]
- H. Steck and T. Jaakkola.
Bias-corrected bootstrap and model uncertainty.
In Advances in Neural Information Processing Systems 16, 2003.
[pdf]
- A. Corduneanu and T. Jaakkola.
On information regularization.
In Proceedings of the Nineteenth Annual Conference on
Uncertainty in Artificial Intelligence, 2003.
[gzipped postscript]
- H. Steck and T. Jaakkola.
Semi-predictive discretization during model selection.
AI Memo AIM-2003-002, 2003.
[pdf]
- N. Srebro and T. Jaakkola.
Weighted low-rank approximations.
In Proceedings of the Twentieth International Conference on
Machine Learning, 2003.
[pdf]
- N. Srebro and T. Jaakkola.
Generalized low-rank approximations.
AI Memo AIM-2003-001, 2003.
[pdf]
- H. Steck and T. Jaakkola.
On the dirichlet prior and bayesian regularization.
In Advances in Neural Information processing systems 15, 2002.
[gzipped postscript]
- 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. Szummer and T. Jaakkola.
Information regularization with partially labeled data.
In Advances in Neural Information processing systems 15, 2002.
[pdf]
- A. Corduneanu and T. Jaakkola.
Continuation methods for mixing heterogeneous sources.
In Proceedings of the Eighteenth Annual Conference on
Uncertainty in Artificial Intelligence, 2002.
[gzipped postscript]
- H. Steck and T. Jaakkola.
Unsupervised active learning in large domains.
In Proceedings of the Eighteenth Annual Conference on
Uncertainty in Artificial Intelligence, 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]
- A. Corduneanu and T. Jaakkola.
Stable mixing of complete and incomplete information.
MIT AI Memo AIM-2001-030, 2001.
[pdf]
- 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]
- M. Szummer and T. Jaakkola.
Partially labeled classification with markov random walks.
In Advances in Neural Information processing systems 14, 2001.
[postscript]
- T. Jaakkola and H. Siegelmann.
Active information retrieval.
In Advances in Neural Information processing systems 14, pages
777--784, 2001.
[gzipped postscript]
- T. Jaakkola.
Tutorial on variational approximation methods.
In Advanced mean field methods: theory and practice. MIT Press,
2000.
[postscript], [gzipped postscript]
- T. Jaakkola and M. Jordan.
Bayesian parameter estimation via variational methods.
Statistics and Computing, 10:25--37, 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]
- M. Szummer and T. Jaakkola.
Kernel expansions with unlabeled examples.
In Advances in Neural Information Processing Systems 13. MIT
Press, 2000.
[postscript], [gzipped postscript]
- M. Meila and T. Jaakkola.
Tractable bayesian learning of tree belief networks.
In Proceedings of the Sixteenth Annual Conference on Uncertainty
in Artificial Intelligence. Morgan Kaufmann, 2000.
[postscript], [gzipped postscript]
- T. Jebara and T. Jaakkola.
Feature selection and dualities in maximum entropy discrimination.
In Proceedings of the Sixteenth Annual Conference on Uncertainty
in Artificial Intelligence. Morgan Kaufmann, 2000.
[postscript], [gzipped postscript]
- T. Jaakkola, M. Meila, and T. Jebara.
Maximum entropy discrimination.
In Advances in Neural Information Processing Systems 12. MIT
Press, 1999.
[postscript], [gzipped postscript]
- T. Jaakkola, M. Meila, and T. Jebara.
Maximum entropy discrimination.
Technical Report AITR-1668, MIT, 1999.
[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]
- T. Jaakkola and D. Haussler.
Probabilistic kernel regression models.
In Proceedings of the Seventh International Workshop on
Artificial Intelligence and Statistics, 1999.
[postscript], [gzipped postscript]
- T. Jaakkola and D. Haussler.
Exploiting generative models in discriminative classifiers.
In Advances in Neural Information Processing Systems 11, 1998.
[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 and M. Jordan.
A variational approach to bayesian logistic regression models and
their extensions.
In Proceedings of the Sixth International Workshop on Artificial
Intelligence and Statistics, 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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