Tommi Jaakkola
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An introduction to variational methods for graphical models
MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul
Machine learning 37 (2), 183-233, 1999
34041999
Exploiting generative models in discriminative classifiers
T Jaakkola, D Haussler
Advances in neural information processing systems, 487-493, 1999
18661999
Maximum-margin matrix factorization
N Srebro, J Rennie, TS Jaakkola
Advances in neural information processing systems, 1329-1336, 2005
11662005
Convergence of stochastic iterative dynamic programming algorithms
T Jaakkola, MI Jordan, SP Singh
Advances in neural information processing systems, 703-710, 1994
9101994
Weighted low-rank approximations
N Srebro, T Jaakkola
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
8492003
Serial regulation of transcriptional regulators in the yeast cell cycle
I Simon, J Barnett, N Hannett, CT Harbison, NJ Rinaldi, TL Volkert, ...
Cell 106 (6), 697-708, 2001
7662001
MAP estimation via agreement on trees: message-passing and linear programming
MJ Wainwright, TS Jaakkola, AS Willsky
IEEE transactions on information theory 51 (11), 3697-3717, 2005
7552005
Computational discovery of gene modules and regulatory networks
Z Bar-Joseph, GK Gerber, TI Lee, NJ Rinaldi, JY Yoo, F Robert, ...
Nature biotechnology 21 (11), 1337-1342, 2003
7472003
Partially labeled classification with Markov random walks
M Szummer, T Jaakkola
Advances in neural information processing systems, 945-952, 2002
7472002
Convergence results for single-step on-policy reinforcement-learning algorithms
S Singh, T Jaakkola, ML Littman, C Szepesvári
Machine learning 38 (3), 287-308, 2000
7302000
A discriminative framework for detecting remote protein homologies
T Jaakkola, M Diekhans, D Haussler
Journal of computational biology 7 (1-2), 95-114, 2000
6462000
Bayesian parameter estimation via variational methods
TS Jaakkola, MI Jordan
Statistics and Computing 10 (1), 25-37, 2000
6262000
Approximate inference in additive factorial hmms with application to energy disaggregation
JZ Kolter, T Jaakkola
Artificial intelligence and statistics, 1472-1482, 2012
5682012
Using the Fisher kernel method to detect remote protein homologies.
TS Jaakkola, M Diekhans, D Haussler
ISMB 99, 149-158, 1999
5661999
An introduction to variational methods for graphical models
MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul
Learning in graphical models, 105-161, 1998
5541998
Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks
AJ Hartemink, DK Gifford, TS Jaakkola, RA Young
Biocomputing 2001, 422-433, 2000
5402000
Optimization for Machine Learning
F Bach, R Jenatton, J Mairal, G Obozinski, M Andersen, J Dahl, Z Liu, ...
MIT Press, 2011
525*2011
Mean field theory for sigmoid belief networks
LK Saul, T Jaakkola, MI Jordan
Journal of artificial intelligence research 4, 61-76, 1996
4841996
A new class of upper bounds on the log partition function
MJ Wainwright, TS Jaakkola, AS Willsky
IEEE Transactions on Information Theory 51 (7), 2313-2335, 2005
4832005
Learning without state-estimation in partially observable Markovian decision processes
SP Singh, T Jaakkola, MI Jordan
Machine Learning Proceedings 1994, 284-292, 1994
4791994
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