Tommi Jaakkola

	2012
86		Yu Xin, Tommi Jaakkola: Primal-Dual methods for sparse constrained matrix completion. Journal of Machine Learning Research - Proceedings Track 22: 1323-1331 (2012)
85		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)
	2011
84		Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network CoRR abs/1105.5462: (2011)
	2010
83		Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi Jaakkola: Collaborative future event recommendation. CIKM 2010: 819-828
82		Alexander M. Rush, David Sontag, Michael Collins, Tommi Jaakkola: On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP 2010: 1-11
81		Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, David Sontag: Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP 2010: 1288-1298
80		Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson: Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790
79		David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson: More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189
78		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)
77		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)
	2009
76		David Sontag, Tommi Jaakkola: Tree Block Coordinate Descent for MAP in Graphical Models. Journal of Machine Learning Research - Proceedings Track 5: 544-551 (2009)
	2008
75		David Sontag, Amir Globerson, Tommi Jaakkola: Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544
74		David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510
73		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)
	2007
72		Amir Globerson, Tommi Jaakkola: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007
71		David Sontag, Tommi Jaakkola: New Outer Bounds on the Marginal Polytope. NIPS 2007
70		Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140
69		Amir Globerson, Tommi Jaakkola: Approximate inference using conditional entropy decompositions. Journal of Machine Learning Research - Proceedings Track 2: 130-138 (2007)
68		Harald Steck, Tommi Jaakkola: Predictive Discretization during Model Selection. Journal of Machine Learning Research - Proceedings Track 2: 532-539 (2007)
67		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)
	2006
66		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
65		Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi Jaakkola: Game Theoretic Algorithms for Protein-DNA binding. NIPS 2006: 1081-1088
64		Yuan (Alan) Qi, Tommi Jaakkola: Parameter Expanded Variational Bayesian Methods. NIPS 2006: 1097-1104
63		Amir Globerson, Tommi Jaakkola: Approximate inference using planar graph decomposition. NIPS 2006: 473-480
62		Chen-Hsiang Yeang, Tommi Jaakkola: Modeling the Combinatorial Functions of Multiple Transcription Factors. Journal of Computational Biology 13(2): 463-480 (2006)
61		Marina Meila, Tommi Jaakkola: Tractable Bayesian learning of tree belief networks. Statistics and Computing 16(1): 77-92 (2006)
	2005
60		Chen-Hsiang Yeang, Tommi Jaakkola: Modeling the Combinatorial Functions of Multiple Transcription Factors. RECOMB 2005: 506-521
59		Jason D. M. Rennie, Tommi Jaakkola: Using term informativeness for named entity detection. SIGIR 2005: 353-360
58		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)
57		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)
56		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)
55		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
54		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
53		Harald Steck, Tommi Jaakkola: Predictive Discretization During Model Selection. DAGM-Symposium 2004: 1-8
52		Adrian Corduneanu, Tommi Jaakkola: Distributed Information Regularization on Graphs. NIPS 2004
51		Nathan Srebro, Noga Alon, Tommi Jaakkola: Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS 2004
50		Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola: Maximum-Margin Matrix Factorization. NIPS 2004
49		Chen-Hsiang Yeang, Trey Ideker, Tommi Jaakkola: Physical Network Models. Journal of Computational Biology 11(2/3): 243-262 (2004)
48		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
47		Chen-Hsiang Yeang, Tommi Jaakkola: Time Series Analysis of Gene Expression and Location Data. BIBE 2003: 305-312
46		Nathan Srebro, Tommi Jaakkola: Weighted Low-Rank Approximations. ICML 2003: 720-727
45		Harald Steck, Tommi Jaakkola: Bias-Corrected Bootstrap and Model Uncertainty. NIPS 2003
44		Nathan Srebro, Tommi Jaakkola: Linear Dependent Dimensionality Reduction. NIPS 2003
43		Claire Monteleoni, Tommi Jaakkola: Online Learning of Non-stationary Sequences. NIPS 2003
42		Chen-Hsiang Yeang, Tommi Jaakkola: Physical network models and multi-source data integration. RECOMB 2003: 312-321
41		Adrian Corduneanu, Tommi Jaakkola: On Information Regularization. UAI 2003: 151-158
40		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)
39		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)
38		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)
	2002
37		Martin Szummer, Tommi Jaakkola: Information Regularization with Partially Labeled Data. NIPS 2002: 1025-1032
36		Harald Steck, Tommi Jaakkola: On the Dirichlet Prior and Bayesian Regularization. NIPS 2002: 697-704
35		Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Exact MAP Estimates by (Hyper)tree Agreement. NIPS 2002: 809-816
34		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
33		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
32		Adrian Corduneanu, Tommi Jaakkola: Continuation Methods for Mixing Heterogenous Sources. UAI 2002: 111-118
31		Harald Steck, Tommi Jaakkola: Unsupervised Active Learning in Large Domains. UAI 2002: 469-476
30		Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: A New Class of upper Bounds on the Log Partition Function. UAI 2002: 536-543
29		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
28		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
27		Ziv Bar-Joseph, David K. Gifford, Tommi Jaakkola: Fast optimal leaf ordering for hierarchical clustering. ISMB (Supplement of Bioinformatics) 2001: 22-29
26		Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky: Tree-based reparameterization for approximate inference on loopy graphs. NIPS 2001: 1001-1008
25		Tommi Jaakkola, Hava T. Siegelmann: Active Information Retrieval. NIPS 2001: 777-784
24		Martin Szummer, Tommi Jaakkola: Partially labeled classification with Markov random walks. NIPS 2001: 945-952
23		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
22		Brendan J. Frey, Relu Patrascu, Tommi Jaakkola, Jodi Moran: Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks. NIPS 2000: 493-499
21		Martin Szummer, Tommi Jaakkola: Kernel Expansions with Unlabeled Examples. NIPS 2000: 626-632
20		Tony Jebara, Tommi Jaakkola: Feature Selection and Dualities in Maximum Entropy Discrimination. UAI 2000: 291-300
19		Marina Meila, Tommi Jaakkola: Tractable Bayesian Learning of Tree Belief Networks. UAI 2000: 380-388
18		Tommi Jaakkola, Mark Diekhans, David Haussler: A Discriminative Framework for Detecting Remote Protein Homologies. Journal of Computational Biology 7(1-2): 95-114 (2000)
17		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
16		Tommi Jaakkola, Mark Diekhans, David Haussler: Using the Fisher Kernel Method to Detect Remote Protein Homologies. ISMB 1999: 149-158
15		Tommi Jaakkola, Marina Meila, Tony Jebara: Maximum Entropy Discrimination. NIPS 1999: 470-476
14		Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999)
13		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
12		Tommi Jaakkola, David Haussler: Exploiting Generative Models in Discriminative Classifiers. NIPS 1998: 487-493
	1997
11		Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997
	1996
10		Tommi Jaakkola, Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493
9		Tommi Jaakkola, Michael I. Jordan: Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996: 340-348
8		Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks CoRR cs.AI/9603102: (1996)
7		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
6		Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534
	1994
5		Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994: 284-292
4		Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352
3		Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368
2		Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation 6(6): 1185-1201 (1994)
	1993
1		Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710

Coauthor Index

1	Noga Alon	[51]
2	Robert C. Altshuler	[78]
3	Ziv Bar-Joseph	[27] [29] [33] [38] [40]
4	Christopher M. Bishop	[11]
5	Ben Charrow	[83]
6	Michael Collins	[81] [82]
7	Adrian Corduneanu	[32] [41] [52]
8	Erik D. Demaine	[29] [40]
9	Mark Diekhans	[16] [18]
10	Robin D. Dowell	[67]
11	Brendan J. Frey	[22]
12	Hui Ge	[66]
13	Georg K. Gerber	[33] [38] [67] [78]
14	Zoubin Ghahramani	[13]
15	David K. Gifford	[23] [27] [28] [29] [33] [34] [38] [40] [54] [66] [67] [78]
16	Amir Globerson	[63] [69] [70] [72] [74] [75] [77] [79] [80]
17	Yuchun Guo	[78]
18	Angèle M. Hamel	[29] [40]
19	Alexander J. Hartemink	[23] [28] [34]
20	David Haussler	[12] [16] [18]
21	Craig P. Hunter	[66]
22	Trey Ideker	[49]
23	Tony Jebara	[15] [20]
24	Michael I. Jordan	[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [13] [14] [84]
25	Ashish Kapoor	[66]
26	J. Zico Kolter (Jeremy Z. Kolter)	[85]
27	Terry Koo	[81]
28	Douglas A. Lauffenburger	[54]
29	Neil D. Lawrence	[11]
30	Jonathan Ledlie	[83]
31	Michael L. Littman	[17]
32	Shaun Mahony	[78]
33	Marina Meila	[15] [19] [61] [77]
34	Talya Meltzer	[74]
35	Ofer Meshi	[79] [80]
36	Einat Minkov	[83]
37	Patrycja E. Missiuro	[66]
38	Claire Monteleoni	[43]
39	Jodi Moran	[22]
40	Garry P. Nolan	[54]
41	Sami Nurmi	[73]
42	Luis E. Ortiz	[65]
43	Georgios Papachristoudis	[78]
44	Relu Patrascu	[22]
45	Dana Pe'er	[54]
46	Omar D. Perez	[54]
47	Luis Pérez-Breva	[65]
48	Yuan (Alan) Qi	[64] [66]
49	Jason D. M. Rennie	[50] [59]
50	Alexander M. Rush	[81] [82]
51	Karen Sachs	[54]
52	Lawrence K. Saul	[6] [7] [8] [13]
53	Hava T. Siegelmann	[25]
54	Itamar Simon	[33] [38]
55	Satinder P. Singh	[1] [2] [3] [4] [5] [17]
56	David Sontag	[71] [74] [75] [76] [77] [79] [80] [81] [82]
57	Nathan Srebro (Nati Srebro)	[29] [40] [44] [46] [50] [51]
58	Harald Steck	[31] [36] [45] [53] [68]
59	Csaba Szepesvári	[17]
60	Martin Szummer	[21] [24] [37]
61	Seth J. Teller	[83]
62	Martin J. Wainwright	[26] [30] [35] [39] [48] [56] [57] [58]
63	Yair Weiss	[74]
64	Alan S. Willsky	[26] [30] [35] [39] [48] [56] [57] [58]
65	Yu Xin	[86]
66	Chen-Hsiang Yeang	[42] [47] [49] [55] [60] [62] [65]
67	Richard A. Young	[23] [28] [34]

Colors in the list of coauthors

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