Latent dirichlet allocation DM Blei, AY Ng, MI Jordan the Journal of machine Learning research 3, 993-1022, 2003 | 12465 | 2003 |
On spectral clustering: Analysis and an algorithm AY Ng, MI Jordan, Y Weiss Advances in neural information processing systems 2, 849-856, 2002 | 4406 | 2002 |
Adaptive mixtures of local experts RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991 | 3088 | 1991 |
Hierarchical mixtures of experts and the EM algorithm MI Jordan, RA Jacobs Neural computation 6 (2), 181-214, 1994 | 2629 | 1994 |
Hierarchical dirichlet processes YW Teh, MI Jordan, MJ Beal, DM Blei Journal of the american statistical association 101 (476), 2006 | 2170 | 2006 |
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999 | 2106 | 1999 |
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science-AAAS-Weekly Paper Edition 269 (5232), 1880-1882, 1995 | 1977 | 1995 |
Learning the kernel matrix with semidefinite programming GRG Lanckriet, N Cristianini, P Bartlett, LE Ghaoui, MI Jordan The Journal of Machine Learning Research 5, 27-72, 2004 | 1884 | 2004 |
Distance metric learning with application to clustering with side-information EP Xing, MI Jordan, S Russell, AY Ng Advances in neural information processing systems, 505-512, 2002 | 1856 | 2002 |
Graphical models, exponential families, and variational inference MJ Wainwright, MI Jordan Foundations and Trends® in Machine Learning 1 (1-2), 1-305, 2008 | 1717 | 2008 |
Learning in Graphical Models:[proceedings of the NATO Advanced Study Institute...: Ettore Mairona Center, Erice, Italy, September 27-October 7, 1996] Springer Science & Business Media, 1998 | 1497 | 1998 |
Optimal feedback control as a theory of motor coordination E Todorov, MI Jordan Nature neuroscience 5 (11), 1226-1235, 2002 | 1488 | 2002 |
Matching words and pictures K Barnard, P Duygulu, D Forsyth, N De Freitas, DM Blei, MI Jordan The Journal of Machine Learning Research 3, 1107-1135, 2003 | 1471 | 2003 |
Kalman filtering with intermittent observations B Sinopoli, L Schenato, M Franceschetti, K Poolla, M Jordan, SS Sastry Automatic Control, IEEE Transactions on 49 (9), 1453-1464, 2004 | 1378 | 2004 |
Kernel independent component analysis FR Bach, MI Jordan The Journal of Machine Learning Research 3, 1-48, 2003 | 1367 | 2003 |
Forward models: Supervised learning with a distal teacher MI Jordan, DE Rumelhart Cognitive science 16 (3), 307-354, 1992 | 1365 | 1992 |
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of artificial intelligence research, 1996 | 1329 | 1996 |
An introduction to MCMC for machine learning C Andrieu, N De Freitas, A Doucet, MI Jordan Machine learning 50 (1-2), 5-43, 2003 | 1313 | 2003 |
Loopy belief propagation for approximate inference: An empirical study KP Murphy, Y Weiss, MI Jordan Proceedings of the Fifteenth conference on Uncertainty in artificial ..., 1999 | 1299 | 1999 |
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes A Ng, MI Jordan Advances in Neural Information Processing Systems 14, 841, 2002 | 1213 | 2002 |
Factorial hidden Markov models Z Ghahramani, MI Jordan Machine learning 29 (2-3), 245-273, 1997 | 1122 | 1997 |
Multiple kernel learning, conic duality, and the SMO algorithm FR Bach, GRG Lanckriet, MI Jordan Proceedings of the twenty-first international conference on Machine learning, 6, 2004 | 1109 | 2004 |
Attractor dynamics and parallellism in a connectionist sequential machine MI Jordan Lawrence Erlbaum Associates, 1986 | 1035 | 1986 |
Modeling annotated data DM Blei, MI Jordan Proceedings of the 26th annual international ACM SIGIR conference on ..., 2003 | 939 | 2003 |
Serial order: A parallel distributed processing approach MI Jordan Advances in psychology 121, 471-495, 1997 | 828 | 1997 |
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006 | 674 | 2006 |
Hierarchical topic models and the nested Chinese restaurant process D Griffiths, M Tenenbaum Advances in neural information processing systems 16, 17, 2004 | 644 | 2004 |
Scalable statistical bug isolation B Liblit, M Naik, AX Zheng, A Aiken, MI Jordan ACM SIGPLAN Notices 40 (6), 15-26, 2005 | 632 | 2005 |
On convergence properties of the EM algorithm for Gaussian mixtures L Xu, MI Jordan Neural computation 8 (1), 129-151, 1996 | 595 | 1996 |
Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks RA Jacobs, MI Jordan, AG Barto Cognitive Science 15 (2), 219-250, 1991 | 578 | 1991 |
Feature selection for high-dimensional genomic microarray data EP Xing, MI Jordan, RM Karp ICML 1, 601-608, 2001 | 577 | 2001 |
Graphical models MI Jordan Statistical Science, 140-155, 2004 | 570 | 2004 |
Variational inference for Dirichlet process mixtures DM Blei, MI Jordan Bayesian analysis 1 (1), 121-143, 2006 | 557 | 2006 |
A direct formulation for sparse PCA using semidefinite programming A d'Aspremont, L El Ghaoui, MI Jordan, GRG Lanckriet SIAM review 49 (3), 434-448, 2007 | 555 | 2007 |
On the convergence of stochastic iterative dynamic programming algorithms T Jaakkola, MI Jordan, SP Singh Neural computation 6 (6), 1185-1201, 1994 | 540 | 1994 |
Bug isolation via remote program sampling B Liblit, A Aiken, AX Zheng, MI Jordan ACM SIGPLAN Notices 38 (5), 141-154, 2003 | 536 | 2003 |
A statistical framework for genomic data fusion GRG Lanckriet, T De Bie, N Cristianini, MI Jordan, WS Noble Bioinformatics 20 (16), 2626-2635, 2004 | 517 | 2004 |
Supervised learning from incomplete data via an EM approach Z Ghahramani, MI Jordan Advances in neural information processing systems 6, 1994 | 509 | 1994 |
Autonomous inverted helicopter flight via reinforcement learning AY Ng, A Coates, M Diel, V Ganapathi, J Schulte, B Tse, E Berger, ... Experimental Robotics IX, 363-372, 2006 | 449* | 2006 |
Convex and semi-nonnegative matrix factorizations C Ding, T Li, M Jordan Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (1), 45-55, 2010 | 409 | 2010 |
Chemogenomic profiling: identifying the functional interactions of small molecules in yeast G Giaever, P Flaherty, J Kumm, M Proctor, C Nislow, DF Jaramillo, ... Proceedings of the National Academy of Sciences of the United States of ..., 2004 | 395 | 2004 |
Sensorimotor adaptation in speech production JF Houde, MI Jordan Science 279 (5354), 1213-1216, 1998 | 394 | 1998 |
Probabilistic independence networks for hidden Markov probability models P Smyth, D Heckerman, MI Jordan Neural computation 9 (2), 227-269, 1997 | 359 | 1997 |
PEGASUS: A policy search method for large MDPs and POMDPs AY Ng, M Jordan Proceedings of the Sixteenth conference on Uncertainty in artificial ..., 2000 | 357 | 2000 |
Bayesian parameter estimation via variational methods TS Jaakkola, MI Jordan Statistics and Computing 10 (1), 25-37, 2000 | 355 | 2000 |
Learning Without State-Estimation in Partially Observable Markovian Decision Processes. SP Singh, T Jaakkola, MI Jordan ICML, 284-292, 1994 | 342 | 1994 |
Learning spectral clustering F Jordan, F Bach Adv. Neural Inf. Process. Syst 16, 305-312, 2004 | 336 | 2004 |
Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study DM Wolpert, Z Ghahramani, MI Jordan Experimental brain research 103 (3), 460-470, 1995 | 319 | 1995 |
Mean field theory for sigmoid belief networks LK Saul, T Jaakkola, MI Jordan Journal of artificial intelligence research 4 (1), 61-76, 1996 | 312 | 1996 |
Convergence results for the EM approach to mixtures of experts architectures MI Jordan, L Xu Neural networks 8 (9), 1409-1431, 1995 | 311 | 1995 |
A robust minimax approach to classification GRG Lanckriet, LE Ghaoui, C Bhattacharyya, MI Jordan The Journal of Machine Learning Research 3, 555-582, 2003 | 309 | 2003 |
Reinforcement learning algorithm for partially observable Markov decision problems T Michael, I Jordan Proceedings of the Advances in Neural Information Processing Systems, 345-352, 1995 | 304 | 1995 |
KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST Online supplement at http://www. noble. gs. washington. edu/yeast. GRG Lanckriet, M Deng, N Cristianini, MI Jordan Proceedings of the Pacific Symposium 6, 10, 2004 | 298 | 2004 |
50 strategies for teaching English language learners AL Herrell, ML Jordan Pearson, 2015 | 296 | 2015 |
The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies DM Blei, TL Griffiths, MI Jordan Journal of the ACM (JACM) 57 (2), 7, 2010 | 296 | 2010 |
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces K Fukumizu, FR Bach, MI Jordan The Journal of Machine Learning Research 5, 73-99, 2004 | 292 | 2004 |
Stable algorithms for link analysis AY Ng, AX Zheng, MI Jordan Proceedings of the 24th annual international ACM SIGIR conference on ..., 2001 | 275 | 2001 |
A comment on D PL Bartlett, MI Jordan, JD Mcauliffe V. Lindley’s statistical paradox’, Biometrika, 1957 | 272 | 1957 |
Joint covariate selection and joint subspace selection for multiple classification problems G Obozinski, B Taskar, MI Jordan Statistics and Computing 20 (2), 231-252, 2010 | 261 | 2010 |
Detecting large-scale system problems by mining console logs W Xu, L Huang, A Fox, D Patterson, MI Jordan Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles ..., 2009 | 261 | 2009 |
Learning with mixtures of trees M Meila, MI Jordan The Journal of Machine Learning Research 1, 1-48, 2001 | 259 | 2001 |
A probabilistic interpretation of canonical correlation analysis FR Bach, MI Jordan Technical Report 688, Department of Statistics, University of California ..., 2005 | 254 | 2005 |
Learning from dyadic data T Hofmann, J Puzicha, MI Jordan Advances in neural information processing systems, 466-472, 1999 | 247 | 1999 |
DiscLDA: Discriminative learning for dimensionality reduction and classification S Lacoste-Julien, F Sha, MI Jordan Advances in neural information processing systems, 897-904, 2009 | 242 | 2009 |
Supervised learning and systems with excess degrees of freedom MI Jordan University of Massachusetts, 1988 | 241 | 1988 |
Reinforcement learning with soft state aggregation SP Singh, T Jaakkola, MI Jordan Advances in neural information processing systems, 361-368, 1995 | 239 | 1995 |
Latent dirichlet allocation DM Blei, AY Ng, MI Jordan Advances in neural information processing systems, 601-608, 2001 | 234 | 2001 |
Failure diagnosis using decision trees M Chen, AX Zheng, J Lloyd, M Jordan, E Brewer Autonomic Computing, 2004. Proceedings. International Conference on, 36-43, 2004 | 230 | 2004 |
Advances in Neural Information Processing Systems 3 Morgan Kaufmann, 1991 | 227 | 1991 |
Hierarchical beta processes and the Indian buffet process R Thibaux, MI Jordan International conference on artificial intelligence and statistics, 564-571, 2007 | 226 | 2007 |
Link analysis, eigenvectors and stability AY Ng, AX Zheng, MI Jordan International Joint Conference on Artificial Intelligence 17 (1), 903-910, 2001 | 226 | 2001 |
An alternative model for mixtures of experts L Xu, MI Jordan, GE Hinton Advances in neural information processing systems, 633-640, 1995 | 225 | 1995 |
Exploiting tractable substructures in intractable networks LK Saul, MI Jordan Advances in neural information processing systems, 486-492, 1996 | 220 | 1996 |
Learning piecewise control strategies in a modular neural network architecture R Jacobs, M Jordan Systems, Man and Cybernetics, IEEE Transactions on 23 (2), 337-345, 1993 | 216 | 1993 |
Motor learning and the degrees of freedom problem. MI Jordan Lawrence Erlbaum Associates, Inc, 1990 | 210 | 1990 |
Hierarchies of adaptive experts MI Jordan, RA Jacobs Advances in neural information processing systems, 985-992, 1992 | 206 | 1992 |
Fast approximate spectral clustering D Yan, L Huang, MI Jordan Proceedings of the 15th ACM SIGKDD international conference on Knowledge ..., 2009 | 201 | 2009 |
Loopy belief propagation and Gibbs measures SC Tatikonda, MI Jordan Proceedings of the Eighteenth conference on Uncertainty in artificial ..., 2002 | 199 | 2002 |
Managing data transfers in computer clusters with orchestra M Chowdhury, M Zaharia, J Ma, MI Jordan, I Stoica ACM SIGCOMM Computer Communication Review 41 (4), 98-109, 2011 | 198 | 2011 |
Hierarchical Bayesian nonparametric models with applications YW Teh, MI Jordan Bayesian nonparametrics 1, 2010 | 197 | 2010 |
Learning spectral clustering, with application to speech separation FR Bach, MI Jordan The Journal of Machine Learning Research 7, 1963-2001, 2006 | 194 | 2006 |
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence L Peņa-Castillo, M Taşan, CL Myers, H Lee, T Joshi, C Zhang, Y Guan, ... Genome biology 9, S2, 2008 | 193 | 2008 |
Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements E Todorov, MI Jordan Journal of Neurophysiology 80 (2), 696-714, 1998 | 193 | 1998 |
Computational motor control MI Jordan, DM Wolpert MIT Press, 1999 | 188 | 1999 |
Nonparametric latent feature models for link prediction K Miller, MI Jordan, TL Griffiths Advances in neural information processing systems, 1276-1284, 2009 | 186 | 2009 |
Constrained and unconstrained movements involve different control strategies M Desmurget, M Jordan, C Prablanc, M Jeannerod Journal of neurophysiology 77 (3), 1644-1650, 1997 | 185 | 1997 |
Learning dependency-based compositional semantics P Liang, MI Jordan, D Klein Computational Linguistics 39 (2), 389-446, 2013 | 183 | 2013 |
Learning from incomplete data Z Ghahramani, MI Jordan | 183 | 1995 |
Toward a protein profile of Escherichia coli: comparison to its transcription profile RW Corbin, O Paliy, F Yang, J Shabanowitz, M Platt, CE Lyons, K Root, ... Proceedings of the National Academy of Sciences 100 (16), 9232-9237, 2003 | 176 | 2003 |
Multi-task feature selection G Obozinski, B Taskar, M Jordan Statistics Department, UC Berkeley, Tech. Rep, 2006 | 175 | 2006 |
Generalization bounds for the area under the ROC curve S Agarwal, T Graepel, R Herbrich, S Har-Peled, D Roth Journal of Machine Learning Research, 393-425, 2005 | 175 | 2005 |
Computational aspects of motor control and motor learning MI Jordan Handbook of perception and action: motor skills 2, 71-118, 1996 | 173 | 1996 |
Gradient following without back-propagation in layered networks AG Barto, MI Jordan et-al. Frontiers in cognitive neuroscience, 443-449, 1992 | 171 | 1992 |
A competitive modular connectionist architecture RA Jacobs, MI Jordan Advances in neural information processing systems, 767-773, 1991 | 170 | 1991 |
Generic constraints on underspecified target trajectories M Jordan Neural Networks, 1989. IJCNN., International Joint Conference on, 217-225, 1989 | 165 | 1989 |
Protein molecular function prediction by Bayesian phylogenomics BE Engelhardt, MI Jordan, KE Muratore, SE Brenner PLoS Comput Biol 1 (5), e45, 2005 | 163 | 2005 |
Predicting multiple metrics for queries: Better decisions enabled by machine learning A Ganapathi, H Kuno, U Dayal, JL Wiener, A Fox, M Jordan, D Patterson Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on, 592-603, 2009 | 162 | 2009 |
An HDP-HMM for systems with state persistence EB Fox, EB Sudderth, MI Jordan, AS Willsky Proceedings of the 25th international conference on Machine learning, 312-319, 2008 | 160 | 2008 |
An introduction to probabilistic graphical models MI Jordan preparation, 2003 | 160 | 2003 |
Mixed memory markov models: Decomposing complex stochastic processes as mixtures of simpler ones LK Saul, MI Jordan Machine learning 37 (1), 75-87, 1999 | 158 | 1999 |