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Note: Links lead to the DBLP on the Web. Tommi Jaakkola 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 : A new class of upper bounds on the log partition function. IEEE Transactions on Information Theory 51 (7): 2313-2335 (2005) 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) Karen Sachs , Omar D. Perez , Dana Peer , 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 Chen-Hsiang Yeang , Trey Ideker , Tommi Jaakkola: Physical Network Models. Journal of Computational Biology 11 (2/3): 243-262 (2004) 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 Harald Steck , Tommi Jaakkola: Bias-Corrected Bootstrap and Model Uncertainty. NIPS 2003 Nathan Srebro , Tommi Jaakkola: Linear Dependent Dimensionality Reduction. NIPS 2003 Claire Monteleoni , Tommi Jaakkola: Online Learning of Non-stationary Sequences. 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 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) 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) Ziv Bar-Joseph , Georg 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 Szummer , Tommi Jaakkola: Information Regularization with Partially Labeled Data. NIPS 2002 : 1025-1032 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 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 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 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) Ziv Bar-Joseph , David K. Gifford , Tommi Jaakkola: Fast optimal leaf ordering for hierarchical clustering. ISMB (Supplement of Bioinformatics) 2001 : 22-29 Martin J. Wainwright , Tommi Jaakkola, Alan S. Willsky : Tree-based reparameterization for approximate inference on loopy graphs. NIPS 2001 : 1001-1008 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 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 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, 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) 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, 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, 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 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) 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, Lawrence K. Saul , Michael I. Jordan : Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995 : 528-534 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 1 [ 49 ] 2 [ 26 ] [ 28 ] [ 32 ] [ 37 ] [ 39 ] 3 [ 10 ] 4 [ 31 ] [ 40 ] [ 50 ] 5 [ 28 ] [ 39 ] 6 [ 15 ] [ 17 ] 7 [ 21 ] 8 [ 32 ] [ 37 ] 9 [ 12 ] 10 [ 22 ] [ 26 ] [ 27 ] [ 28 ] [ 32 ] [ 33 ] [ 37 ] [ 39 ] [ 52 ] 11 [ 28 ] [ 39 ] 12 [ 22 ] [ 27 ] [ 33 ] 13 [ 11 ] [ 15 ] [ 17 ] 14 [ 47 ] 15 [ 14 ] [ 19 ] 16 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 12 ] [ 13 ] 17 [ 52 ] 18 [ 10 ] 19 [ 16 ] 20 [ 14 ] [ 18 ] 21 [ 42 ] 22 [ 21 ] 23 [ 52 ] 24 [ 21 ] 25 [ 52 ] 26 [ 52 ] 27 [ 48 ] [ 55 ] 28 [ 52 ] 29 [ 5 ] [ 6 ] [ 7 ] [ 12 ] 30 [ 24 ] 31 [ 32 ] [ 37 ] 32 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 16 ] 33 [ 28 ] [ 39 ] [ 43 ] [ 45 ] [ 48 ] [ 49 ] 34 [ 30 ] [ 35 ] [ 44 ] [ 51 ] 35 [ 16 ] 36 [ 20 ] [ 23 ] [ 36 ] 37 [ 25 ] [ 29 ] [ 34 ] [ 38 ] [ 54 ] 38 [ 25 ] [ 29 ] [ 34 ] [ 38 ] [ 54 ] 39 [ 41 ] [ 46 ] [ 47 ] [ 53 ] [ 56 ] 40 [ 22 ] [ 27 ] [ 33 ]  ©2006 Association for Computing Machinery |
