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You can view all the papers in reverse
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machine learning,
natural language processing,
biology,
chemistry,
or physics, or papers in more specific areas including
game theory, inference, semi-supervised learning , information retrieval, or
reinforcement learning.
The list does not include all recent preprints from arXiv or
bioRxiv.
For a more complete list, see my
Google scholar page
Inference papers
- Yuan Zhang, Tao Lei, Regina Barzilay, and Tommi Jaakkola.
Greed is Good if Randomized: New Inference for Dependency Parsing.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
[pdf]
- Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, and Tommi Jaakkola.
Low-Rank Tensors for Scoring Dependency Structures.
Association for Computational Linguistics (ACL), 2014.
[pdf]
- Yuan Zhang, Tao Lei, Regina Barzilay, Tommi Jaakkola, and Amir Globerson.
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees.
Association for Computational Linguistics (ACL), 2014.
[pdf]
- Andreea Gane, Tamir Hazan, and Tommi Jaakkola.
Learning with Maximum A-Posteriori Perturbation Models.
Artificial Intelligence and Statistics (AISTATS), 2014.
[pdf]
- Tamir Hazan, Subhransu Maji, and Tommi Jaakkola.
On Sampling from the Gibbs distribution with Random Maximum A Posteriori Perturbations.
Advances of Neural Information Processing Systems (NIPS), 2013.
[pdf]
- Tamir Hazan and Tommi Jaakkola.
On the Partition Function and Random Maximum A-Posteriori Perturbations.
Proceedings of the 29th International Conference on Machine Learning (ICML), 2012.
[pdf]
- Ofer Meshi, Tommi Jaakkola, and Amir Globerson.
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms.
Advances in Neural Information Processing Systems (NIPS), 2012.
- J. Zico Kolter and Tommi Jaakkola.
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation.
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 22, pp. 1472-1482. 2012.
[pdf]
- David Sontag, Amir Globerson, and Tommi Jaakkola.
Introduction to dual decomposition for inference.
S. Sra, S. Nowozin, and S. Wright, Eds., Optimization for Machine Learning, 2010.
[pdf]
- David Sontag, Ofer Meshi, Tommi Jaakkola, and Amir Globerson.
More data means less inference: A pseudo-max approach to structured learning.
Advances in Neural Information Processing Systems (NIPS), 2010.
[pdf]
- Alexander M. Rush, David Sontag, Michael Collins, and Tommi Jaakkola.
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
[pdf]
- Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, and David Sontag.
Dual Decomposition for Parsing with Non-Projective Head Automata.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
[pdf]
- Ofer Meshi, David Sontag, Tommi Jaakkola, and Amir Globerson.
Learning Efficiently with Approximate Inference via Dual Losses.
Proceedings of the 27th International Conference on Machine Learning (ICML), 2010.
[pdf]
- Tommi Jaakkola, David Sontag, Amir Globerson, and Marina Meila.
Learning Bayesian Network Structure using LP Relaxations.
Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
[pdf] [slides]
- David Sontag and Tommi Jaakkola.
Tree Block Coordinate Descent for MAP in Graphical Models.
Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), 2009.
[pdf]
- David Sontag, Amir Globerson, and Tommi Jaakkola.
Clusters and Coarse Partitions in LP Relaxations.
Advances in Neural Information Processing Systems (NIPS), 2008.
[pdf]
- David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, and Yair Weiss.
Tightening LP Relaxations for MAP using Message Passing.
Proceedings of the 24rd Conference on Uncertainty in Artificial Intelligence (AISTATS), 2008.
[pdf]
- David Sontag and Tommi Jaakkola.
New Outer Bounds on the Marginal Polytope.
Advances in Neural Information Processing Systems (NIPS), 2007.
[pdf]
- Amir Globerson and Tommi Jaakkola.
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
Advances in Neural Information Processing Systems (NIPS), 2007.
[pdf]
- Amir Globerson and Tommi Jaakkola.
Convergent Propagation Algorithms via Oriented Trees.
Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI), 2007.
[pdf]
- David Sontag and Tommi Jaakkola.
On Iteratively Constraining the Marginal Polytope for Approximate Inference and MAP.
2007.
[pdf]
- Amir Globerson and Tommi Jaakkola.
Approximate inference using conditional entropy decompositions.
Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
[pdf]
- Amir Globerson and Tommi Jaakkola.
Approximate inference using planar graph decomposition.
Advances in Neural Information Processing Systems (NIPS), 2006.
[pdf]
- Martin J. Wainwright, Tommi S. Jaakkola, and Alan S. Willsky.
MAP estimation via agreement on trees: Message-passing and linear-programming approaches.
IEEE Transactions on Information Theory, 51(11), pp. 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, pp. 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), pp. 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.
Advances in Neural Information processing systems (NIPS), 2002.
[ps.gz]
- M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
A new class of upper bounds on the log partition function.
Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2002.
[ps.gz]
- M. Wainwright, T. Jaakkola, and A. Willsky.
Tree-based reparameterization for approximate estimation on loopy graphs.
Advances in Neural Information processing systems (NIPS), 2001.
[pdf]
- M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
Tree-based reparameterization framework for approximate estimation in graphs with cycles.
2001.
[ps.gz]
- T. Jaakkola.
Tutorial on variational approximation methods.
Advanced mean field methods: theory and practice, 2000.
[ps]
- B. Frey, R. Patrascu, T. Jaakkola, and J. Moran.
Sequentially fitting inclusive trees for inference in Noisy-OR networks.
Advances in Neural Information Processing Systems (NIPS), 2000.
[ps]
- T. Jaakkola and M. Jordan.
Variational probabilistic inference and the QMR-DT database.
Journal of Artificial Intelligence Research, 10, pp. 291–322. 1999.
[ps] [pdf]
- M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
An Introduction to Variational Methods for Graphical Models.
Machine Learning, 37(2), pp. 183. 1999.
[ps]
- C. Bishop, N. Lawrence, T. Jaakkola, and M. Jordan.
Approximating posterior distributions in Belief networks using mixtures.
Advances in Neural Information Processing Systems (NIPS), 1997.
[ps]
- T. Jaakkola.
Variational methods for inference and estimation in graphical models.
1997.
[ps]
- T. Jaakkola and M. Jordan.
Improving the mean field approximation via the use of mixture distributions.
Proceedings of the NATO ASI on Learning in Graphical Models, 1997.
[ps]
- L. Saul, T. Jaakkola, and M. Jordan.
Mean field theory for sigmoid belief networks.
Journal of Artificial Intelligence Research, 4, pp. 61–76. 1996.
[ps] [pdf]
- T. Jaakkola and M. Jordan.
Recursive algorithms for approximating probabilities in graphical models.
Advances in Neural Information Processing Systems (NIPS), 1996.
[ps]
- T. Jaakkola and M. Jordan.
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks.
Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 340–348. 1996.
[ps]
- T. Jaakkola, L. Saul, and M. Jordan.
Fast learning by bounding likelihoods in sigmoid type belief networks.
Advances in Neural Information Processing Systems (NIPS), 1995.
[ps]
