| An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999 | 3499 | 1999 |
| Exploiting generative models in discriminative classifiers T Jaakkola, D Haussler Advances in neural information processing systems, 487-493, 1999 | 1885 | 1999 |
| Maximum-margin matrix factorization N Srebro, J Rennie, TS Jaakkola Advances in neural information processing systems, 1329-1336, 2005 | 1189 | 2005 |
| Convergence of stochastic iterative dynamic programming algorithms T Jaakkola, MI Jordan, SP Singh Advances in neural information processing systems, 703-710, 1994 | 937 | 1994 |
| Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003 | 865 | 2003 |
| 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 | 770 | 2001 |
| 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 | 761 | 2005 |
| Partially labeled classification with Markov random walks M Szummer, T Jaakkola Advances in neural information processing systems, 945-952, 2002 | 760 | 2002 |
| 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 | 749 | 2003 |
| 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 | 746 | 2000 |
| A discriminative framework for detecting remote protein homologies T Jaakkola, M Diekhans, D Haussler Journal of computational biology 7 (1-2), 95-114, 2000 | 649 | 2000 |
| Bayesian parameter estimation via variational methods TS Jaakkola, MI Jordan Statistics and Computing 10 (1), 25-37, 2000 | 639 | 2000 |
| Approximate inference in additive factorial hmms with application to energy disaggregation JZ Kolter, T Jaakkola Artificial intelligence and statistics, 1472-1482, 2012 | 585 | 2012 |
| Using the Fisher kernel method to detect remote protein homologies. TS Jaakkola, M Diekhans, D Haussler ISMB 99, 149-158, 1999 | 568 | 1999 |
| An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Learning in graphical models, 105-161, 1998 | 560 | 1998 |
| 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 | 541 | 2000 |
| Optimization for Machine Learning F Bach, R Jenatton, J Mairal, G Obozinski, M Andersen, J Dahl, Z Liu, ... MIT Press, 2011 | 540* | 2011 |
| 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 | 504 | 2005 |
| Mean field theory for sigmoid belief networks LK Saul, T Jaakkola, MI Jordan Journal of artificial intelligence research 4, 61-76, 1996 | 500 | 1996 |
| Learning without state-estimation in partially observable Markovian decision processes SP Singh, T Jaakkola, MI Jordan Machine Learning Proceedings 1994, 284-292, 1994 | 490 | 1994 |
