Tommi Jaakkola

Adrian Corduneanu, Tommi Jaakkola: Continuation Methods for Mixing Heterogenous Sources. CoRR abs/1301.0562 (2013)

Harald Steck, Tommi Jaakkola: Unsupervised Active Learning in Large Domains. CoRR abs/1301.0602 (2013)

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: A New Class of Upper Bounds on the Log Partition Function. CoRR abs/1301.0610 (2013)

Tony Jebara, Tommi Jaakkola: Feature Selection and Dualities in Maximum Entropy Discrimination. CoRR abs/1301.3865 (2013)

Marina Meila, Tommi Jaakkola: Tractable Bayesian Learning of Tree Belief Networks. CoRR abs/1301.3875 (2013)

Tommi Jaakkola, Michael I. Jordan: Computing Upper and Lower Bounds on Likelihoods in Intractable Networks. CoRR abs/1302.3586 (2013)

Tatsunori Hashimoto, Tommi Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David K. Gifford: Lineage-based identification of cellular states and expression programs. Bioinformatics 28(12): 250-257 (2012)

Teemu Roos, Petri Myllymäki, Tommi Jaakkola: Special Issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). Int. J. Approx. Reasoning 53(9): 1303-1304 (2012)

Yu Xin, Tommi Jaakkola: Primal-Dual methods for sparse constrained matrix completion. Journal of Machine Learning Research - Proceedings Track 22: 1323-1331 (2012)

J. Zico Kolter, Tommi Jaakkola: Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. Journal of Machine Learning Research - Proceedings Track 22: 1472-1482 (2012)

Tamir Hazan, Tommi Jaakkola: On the Partition Function and Random Maximum A-Posteriori Perturbations. ICML 2012

Ofer Meshi, Tommi Jaakkola, Amir Globerson: Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS 2012: 3023-3031

David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. CoRR abs/1206.3288 (2012)

Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. CoRR abs/1206.5243 (2012)

Fahiem Bacchus, Tommi Jaakkola: Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005). CoRR abs/1208.5159 (2012)

Adrian Corduneanu, Tommi Jaakkola: On Information Regularization. CoRR abs/1212.2466 (2012)

Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. CoRR abs/1105.5462 (2011)

Yuchun Guo, Georgios Papachristoudis, Robert C. Altshuler, Georg K. Gerber, Tommi Jaakkola, David K. Gifford, Shaun Mahony: Discovering homotypic binding events at high spatial resolution. Bioinformatics 26(24): 3028-3034 (2010)

Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila: Learning Bayesian Network Structure using LP Relaxations. Journal of Machine Learning Research - Proceedings Track 9: 358-365 (2010)

Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi Jaakkola: Collaborative future event recommendation. CIKM 2010: 819-828

Alexander M. Rush, David Sontag, Michael Collins, Tommi Jaakkola: On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP 2010: 1-11

Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, David Sontag: Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP 2010: 1288-1298

Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson: Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790

David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson: More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189

David Sontag, Tommi Jaakkola: Tree Block Coordinate Descent for MAP in Graphical Models. Journal of Machine Learning Research - Proceedings Track 5: 544-551 (2009)

Tommi Jaakkola, Sami Nurmi: Fostering elementary school students' understanding of simple electricity by combining simulation and laboratory activities. J. Comp. Assisted Learning 24(4): 271-283 (2008)

David Sontag, Amir Globerson, Tommi Jaakkola: Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544

David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510

Amir Globerson, Tommi Jaakkola: Approximate inference using conditional entropy decompositions. Journal of Machine Learning Research - Proceedings Track 2: 130-138 (2007)

Harald Steck, Tommi Jaakkola: Predictive Discretization during Model Selection. Journal of Machine Learning Research - Proceedings Track 2: 532-539 (2007)

Georg K. Gerber, Robin D. Dowell, Tommi Jaakkola, David K. Gifford: Automated Discovery of Functional Generality of Human Gene Expression Programs. PLoS Computational Biology 3(8) (2007)

Amir Globerson, Tommi Jaakkola: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007

David Sontag, Tommi Jaakkola: New Outer Bounds on the Marginal Polytope. NIPS 2007

Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140

Chen-Hsiang Yeang, Tommi Jaakkola: Modeling the Combinatorial Functions of Multiple Transcription Factors. Journal of Computational Biology 13(2): 463-480 (2006)

Marina Meila, Tommi Jaakkola: Tractable Bayesian learning of tree belief networks. Statistics and Computing 16(1): 77-92 (2006)

Yuan (Alan) Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi Jaakkola, David K. Gifford, Hui Ge: Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo. ISMB (Supplement of Bioinformatics) 2006: 417-423

Amir Globerson, Tommi Jaakkola: Approximate inference using planar graph decomposition. NIPS 2006: 473-480

Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi Jaakkola: Game Theoretic Algorithms for Protein-DNA binding. NIPS 2006: 1081-1088

Yuan (Alan) Qi, Tommi Jaakkola: Parameter Expanded Variational Bayesian Methods. NIPS 2006: 1097-1104

Chen-Hsiang Yeang, Tommi Jaakkola: Time Series Analysis of Gene Expression and Location Data. International Journal on Artificial Intelligence Tools 14(5): 755-770 (2005)

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: A new class of upper bounds on the log partition function. IEEE Transactions on Information Theory 51(7): 2313-2335 (2005)

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory 51(11): 3697-3717 (2005)

Chen-Hsiang Yeang, Tommi Jaakkola: Modeling the Combinatorial Functions of Multiple Transcription Factors. RECOMB 2005: 506-521

Jason D. M. Rennie, Tommi Jaakkola: Using term informativeness for named entity detection. SIGIR 2005: 353-360

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: MAP estimation via agreement on (hyper)trees: Message-passing and linear programming. CoRR abs/cs/0508070 (2005)

Chen-Hsiang Yeang, Trey Ideker, Tommi Jaakkola: Physical Network Models. Journal of Computational Biology 11(2/3): 243-262 (2004)

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Tree consistency and bounds on the performance of the max-product algorithm and its generalizations. Statistics and Computing 14(2): 143-166 (2004)

Karen Sachs, Omar D. Perez, Dana Pe'er, Garry P. Nolan, David K. Gifford, Tommi Jaakkola, Douglas A. Lauffenburger: Analysis of Signaling Pathways in Human T-Cells Using Bayesian Network Modeling of Single Cell Data. CSB 2004: 644

Harald Steck, Tommi Jaakkola: Predictive Discretization During Model Selection. DAGM-Symposium 2004: 1-8

Adrian Corduneanu, Tommi Jaakkola: Distributed Information Regularization on Graphs. NIPS 2004

Nathan Srebro, Noga Alon, Tommi Jaakkola: Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS 2004

Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola: Maximum-Margin Matrix Factorization. NIPS 2004

Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi Jaakkola: K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. Bioinformatics 19(9): 1070-1078 (2003)

Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi Jaakkola, Itamar Simon: Continuous Representations of Time-Series Gene Expression Data. Journal of Computational Biology 10(3/4): 341-356 (2003)

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Tree-based reparameterization framework for analysis of sum-product and related algorithms. IEEE Transactions on Information Theory 49(5): 1120-1146 (2003)

Chen-Hsiang Yeang, Tommi Jaakkola: Time Series Analysis of Gene Expression and Location Data. BIBE 2003: 305-312

Nathan Srebro, Tommi Jaakkola: Weighted Low-Rank Approximations. ICML 2003: 720-727

Claire Monteleoni, Tommi Jaakkola: Online Learning of Non-stationary Sequences. NIPS 2003

Nathan Srebro, Tommi Jaakkola: Linear Dependent Dimensionality Reduction. NIPS 2003

Harald Steck, Tommi Jaakkola: Bias-Corrected Bootstrap and Model Uncertainty. NIPS 2003

Chen-Hsiang Yeang, Tommi Jaakkola: Physical network models and multi-source data integration. RECOMB 2003: 312-321

Adrian Corduneanu, Tommi Jaakkola: On Information Regularization. UAI 2003: 151-158

Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young: Bayesian Methods for Elucidating Genetic Regulatory Networks. IEEE Intelligent Systems 17(2): 37-43 (2002)

Harald Steck, Tommi Jaakkola: On the Dirichlet Prior and Bayesian Regularization. NIPS 2002: 697-704

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Exact MAP Estimates by (Hyper)tree Agreement. NIPS 2002: 809-816

Martin Szummer, Tommi Jaakkola: Information Regularization with Partially Labeled Data. NIPS 2002: 1025-1032

Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young: Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. Pacific Symposium on Biocomputing 2002: 437-449

Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi Jaakkola, Itamar Simon: A new approach to analyzing gene expression time series data. RECOMB 2002: 39-48

Adrian Corduneanu, Tommi Jaakkola: Continuation Methods for Mixing Heterogenous Sources. UAI 2002: 111-118

Harald Steck, Tommi Jaakkola: Unsupervised Active Learning in Large Domains. UAI 2002: 469-476

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: A New Class of upper Bounds on the Log Partition Function. UAI 2002: 536-543

Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi Jaakkola, Nathan Srebro: K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. WABI 2002: 506-520

Ziv Bar-Joseph, David K. Gifford, Tommi Jaakkola: Fast optimal leaf ordering for hierarchical clustering. ISMB (Supplement of Bioinformatics) 2001: 22-29

Tommi Jaakkola, Hava T. Siegelmann: Active Information Retrieval. NIPS 2001: 777-784

Martin Szummer, Tommi Jaakkola: Partially labeled classification with Markov random walks. NIPS 2001: 945-952

Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Tree-based reparameterization for approximate inference on loopy graphs. NIPS 2001: 1001-1008

Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young: Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. Pacific Symposium on Biocomputing 2001: 422-433

Tommi Jaakkola, Mark Diekhans, David Haussler: A Discriminative Framework for Detecting Remote Protein Homologies. Journal of Computational Biology 7(1-2): 95-114 (2000)

Satinder P. Singh, Tommi Jaakkola, Michael L. Littman, Csaba Szepesvári: Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Machine Learning 38(3): 287-308 (2000)

Brendan J. Frey, Relu Patrascu, Tommi Jaakkola, Jodi Moran: Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks. NIPS 2000: 493-499

Martin Szummer, Tommi Jaakkola: Kernel Expansions with Unlabeled Examples. NIPS 2000: 626-632

Tony Jebara, Tommi Jaakkola: Feature Selection and Dualities in Maximum Entropy Discrimination. UAI 2000: 291-300

Marina Meila, Tommi Jaakkola: Tractable Bayesian Learning of Tree Belief Networks. UAI 2000: 380-388

Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999)

Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola, Lawrence K. Saul: An Introduction to Variational Methods for Graphical Models. Machine Learning 37(2): 183-233 (1999)

Tommi Jaakkola, Mark Diekhans, David Haussler: Using the Fisher Kernel Method to Detect Remote Protein Homologies. ISMB 1999: 149-158

Tommi Jaakkola, Marina Meila, Tony Jebara: Maximum Entropy Discrimination. NIPS 1999: 470-476

Tommi Jaakkola, David Haussler: Exploiting Generative Models in Discriminative Classifiers. NIPS 1998: 487-493

Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997

Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996)

Tommi Jaakkola, Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493

Tommi Jaakkola, Michael I. Jordan: Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996: 340-348

Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. CoRR cs.AI/9603102 (1996)

Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534

Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation 6(6): 1185-1201 (1994)

Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994: 284-292

Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352

Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368

Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710