| 2008 |
|---|
| 69 | EE | David Sontag,
Amir Globerson,
Tommi Jaakkola:
Clusters and Coarse Partitions in LP Relaxations.
NIPS 2008: 1537-1544 |
| 68 | EE | David Sontag,
Talya Meltzer,
Amir Globerson,
Tommi Jaakkola,
Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing.
UAI 2008: 503-510 |
| 2007 |
|---|
| 67 | EE | Amir Globerson,
Tommi Jaakkola:
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
NIPS 2007 |
| 66 | EE | David Sontag,
Tommi Jaakkola:
New Outer Bounds on the Marginal Polytope.
NIPS 2007 |
| 2006 |
|---|
| 65 | EE | 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 |
| 64 | EE | Luis Pérez-Breva,
Luis E. Ortiz,
Chen-Hsiang Yeang,
Tommi Jaakkola:
Game Theoretic Algorithms for Protein-DNA binding.
NIPS 2006: 1081-1088 |
| 63 | EE | Yuan (Alan) Qi,
Tommi Jaakkola:
Parameter Expanded Variational Bayesian Methods.
NIPS 2006: 1097-1104 |
| 62 | EE | Amir Globerson,
Tommi Jaakkola:
Approximate inference using planar graph decomposition.
NIPS 2006: 473-480 |
| 61 | EE | Chen-Hsiang Yeang,
Tommi Jaakkola:
Modeling the Combinatorial Functions of Multiple Transcription Factors.
Journal of Computational Biology 13(2): 463-480 (2006) |
| 60 | EE | Marina Meila,
Tommi Jaakkola:
Tractable Bayesian learning of tree belief networks.
Statistics and Computing 16(1): 77-92 (2006) |
| 2005 |
|---|
| 59 | EE | Chen-Hsiang Yeang,
Tommi Jaakkola:
Modeling the Combinatorial Functions of Multiple Transcription Factors.
RECOMB 2005: 506-521 |
| 58 | EE | Jason D. M. Rennie,
Tommi Jaakkola:
Using term informativeness for named entity detection.
SIGIR 2005: 353-360 |
| 57 | EE | 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) |
| 56 | EE | 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) |
| 55 | EE | 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) |
| 54 | EE | 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) |
| 2004 |
|---|
| 53 | EE | 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 |
| 52 | EE | Harald Steck,
Tommi Jaakkola:
Predictive Discretization During Model Selection.
DAGM-Symposium 2004: 1-8 |
| 51 | EE | Adrian Corduneanu,
Tommi Jaakkola:
Distributed Information Regularization on Graphs.
NIPS 2004 |
| 50 | EE | Nathan Srebro,
Noga Alon,
Tommi Jaakkola:
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices.
NIPS 2004 |
| 49 | EE | Nathan Srebro,
Jason D. M. Rennie,
Tommi Jaakkola:
Maximum-Margin Matrix Factorization.
NIPS 2004 |
| 48 | EE | Chen-Hsiang Yeang,
Trey Ideker,
Tommi Jaakkola:
Physical Network Models.
Journal of Computational Biology 11(2/3): 243-262 (2004) |
| 47 | EE | 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) |
| 2003 |
|---|
| 46 | EE | Chen-Hsiang Yeang,
Tommi Jaakkola:
Time Series Analysis of Gene Expression and Location Data.
BIBE 2003: 305-312 |
| 45 | | Nathan Srebro,
Tommi Jaakkola:
Weighted Low-Rank Approximations.
ICML 2003: 720-727 |
| 44 | EE | Harald Steck,
Tommi Jaakkola:
Bias-Corrected Bootstrap and Model Uncertainty.
NIPS 2003 |
| 43 | EE | Nathan Srebro,
Tommi Jaakkola:
Linear Dependent Dimensionality Reduction.
NIPS 2003 |
| 42 | EE | Claire Monteleoni,
Tommi Jaakkola:
Online Learning of Non-stationary Sequences.
NIPS 2003 |
| 41 | EE | Chen-Hsiang Yeang,
Tommi Jaakkola:
Physical network models and multi-source data integration.
RECOMB 2003: 312-321 |
| 40 | | Adrian Corduneanu,
Tommi Jaakkola:
On Information Regularization.
UAI 2003: 151-158 |
| 39 | | 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) |
| 38 | | 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) |
| 37 | EE | 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) |
| 2002 |
|---|
| 36 | EE | Martin Szummer,
Tommi Jaakkola:
Information Regularization with Partially Labeled Data.
NIPS 2002: 1025-1032 |
| 35 | EE | Harald Steck,
Tommi Jaakkola:
On the Dirichlet Prior and Bayesian Regularization.
NIPS 2002: 697-704 |
| 34 | EE | Martin J. Wainwright,
Tommi Jaakkola,
Alan S. Willsky:
Exact MAP Estimates by (Hyper)tree Agreement.
NIPS 2002: 809-816 |
| 33 | EE | 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 |
| 32 | EE | 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 |
| 31 | | Adrian Corduneanu,
Tommi Jaakkola:
Continuation Methods for Mixing Heterogenous Sources.
UAI 2002: 111-118 |
| 30 | | Harald Steck,
Tommi Jaakkola:
Unsupervised Active Learning in Large Domains.
UAI 2002: 469-476 |
| 29 | | Martin J. Wainwright,
Tommi Jaakkola,
Alan S. Willsky:
A New Class of upper Bounds on the Log Partition Function.
UAI 2002: 536-543 |
| 28 | EE | 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 |
| 27 | EE | 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) |
| 2001 |
|---|
| 26 | | Ziv Bar-Joseph,
David K. Gifford,
Tommi Jaakkola:
Fast optimal leaf ordering for hierarchical clustering.
ISMB (Supplement of Bioinformatics) 2001: 22-29 |
| 25 | EE | Martin J. Wainwright,
Tommi Jaakkola,
Alan S. Willsky:
Tree-based reparameterization for approximate inference on loopy graphs.
NIPS 2001: 1001-1008 |
| 24 | EE | Tommi Jaakkola,
Hava T. Siegelmann:
Active Information Retrieval.
NIPS 2001: 777-784 |
| 23 | EE | Martin Szummer,
Tommi Jaakkola:
Partially labeled classification with Markov random walks.
NIPS 2001: 945-952 |
| 22 | EE | 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 |
| 2000 |
|---|
| 21 | | Brendan J. Frey,
Relu Patrascu,
Tommi Jaakkola,
Jodi Moran:
Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks.
NIPS 2000: 493-499 |
| 20 | | Martin Szummer,
Tommi Jaakkola:
Kernel Expansions with Unlabeled Examples.
NIPS 2000: 626-632 |
| 19 | EE | Tony Jebara,
Tommi Jaakkola:
Feature Selection and Dualities in Maximum Entropy Discrimination.
UAI 2000: 291-300 |
| 18 | EE | Marina Meila,
Tommi Jaakkola:
Tractable Bayesian Learning of Tree Belief Networks.
UAI 2000: 380-388 |
| 17 | | Tommi Jaakkola,
Mark Diekhans,
David Haussler:
A Discriminative Framework for Detecting Remote Protein Homologies.
Journal of Computational Biology 7(1-2): 95-114 (2000) |
| 16 | | 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) |
| 1999 |
|---|
| 15 | | Tommi Jaakkola,
Mark Diekhans,
David Haussler:
Using the Fisher Kernel Method to Detect Remote Protein Homologies.
ISMB 1999: 149-158 |
| 14 | EE | Tommi Jaakkola,
Marina Meila,
Tony Jebara:
Maximum Entropy Discrimination.
NIPS 1999: 470-476 |
| 13 | EE | Tommi Jaakkola,
Michael I. Jordan:
Variational Probabilistic Inference and the QMR-DT Network.
J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999) |
| 12 | | 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) |
| 1998 |
|---|
| 11 | EE | Tommi Jaakkola,
David Haussler:
Exploiting Generative Models in Discriminative Classifiers.
NIPS 1998: 487-493 |
| 1997 |
|---|
| 10 | | Christopher M. Bishop,
Neil D. Lawrence,
Tommi Jaakkola,
Michael I. Jordan:
Approximating Posterior Distributions in Belief Networks Using Mixtures.
NIPS 1997 |
| 1996 |
|---|
| 9 | EE | Tommi Jaakkola,
Michael I. Jordan:
Recursive Algorithms for Approximating Probabilities in Graphical Models.
NIPS 1996: 487-493 |
| 8 | EE | Tommi Jaakkola,
Michael I. Jordan:
Computing upper and lower bounds on likelihoods in intractable networks.
UAI 1996: 340-348 |
| 7 | EE | Lawrence K. Saul,
Tommi Jaakkola,
Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks
CoRR cs.AI/9603102: (1996) |
| 6 | | Lawrence K. Saul,
Tommi Jaakkola,
Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks.
J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996) |
| 1995 |
|---|
| 5 | EE | Tommi Jaakkola,
Lawrence K. Saul,
Michael I. Jordan:
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks.
NIPS 1995: 528-534 |
| 1994 |
|---|
| 4 | | Satinder P. Singh,
Tommi Jaakkola,
Michael I. Jordan:
Learning Without State-Estimation in Partially Observable Markovian Decision Processes.
ICML 1994: 284-292 |
| 3 | EE | Tommi Jaakkola,
Satinder P. Singh,
Michael I. Jordan:
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems.
NIPS 1994: 345-352 |
| 2 | EE | Satinder P. Singh,
Tommi Jaakkola,
Michael I. Jordan:
Reinforcement Learning with Soft State Aggregation.
NIPS 1994: 361-368 |
| 1993 |
|---|
| 1 | EE | Tommi Jaakkola,
Michael I. Jordan,
Satinder P. Singh:
Convergence of Stochastic Iterative Dynamic Programming Algorithms.
NIPS 1993: 703-710 |
