Variational probabilistic inference and the QMR-DT network TS Jaakkola, MI Jordan Journal of artificial intelligence research, 291-322, 1999 | 158 | 1999 |
Kernel dimension reduction in regression K Fukumizu, FR Bach, MI Jordan The Annals of Statistics, 1871-1905, 2009 | 157 | 2009 |
The Infinite PCFG Using Hierarchical Dirichlet Processes. P Liang, S Petrov, MI Jordan, D Klein EMNLP-CoNLL, 688-697, 2007 | 157 | 2007 |
A kernel-based learning approach to ad hoc sensor network localization X Nguyen, MI Jordan, B Sinopoli ACM Transactions on Sensor Networks (TOSN) 1 (1), 134-152, 2005 | 153 | 2005 |
A generalized mean field algorithm for variational inference in exponential families EP Xing, MI Jordan, S Russell Proceedings of the Nineteenth conference on Uncertainty in Artificial ..., 2002 | 153 | 2002 |
Predictive low-rank decomposition for kernel methods FR Bach, MI Jordan Proceedings of the 22nd international conference on Machine learning, 33-40, 2005 | 151 | 2005 |
Generalization to local remappings of the visuomotor coordinate transformation Z Ghahramani, DM Wolpert, MI Jordan The Journal of neuroscience 16 (21), 7085-7096, 1996 | 149 | 1996 |
Learning to control an unstable system with forward modeling MI Jordan, RA Jacobs Advances in neural information processing systems, 324-331, 1990 | 146 | 1990 |
A more biologically plausible learning rule for neural networks. P Mazzoni, RA Andersen, MI Jordan Proceedings of the National Academy of Sciences 88 (10), 4433-4437, 1991 | 144 | 1991 |
On feature selection: learning with exponentially many irrevelant features as training examples AY Ng Massachusetts Institute of Technology, Dept. of Electrical Engineering and ..., 1998 | 142 | 1998 |
Trading relations between tongue‐body raising and lip rounding in production of the vowel/u: A pilot ‘‘motor equivalence’’study JS Perkell, ML Matthies, MA Svirsky, MI Jordan The Journal of the Acoustical Society of America 93 (5), 2948-2961, 1993 | 141 | 1993 |
Semi-supervised learning via Gaussian processes ND Lawrence, MI Jordan Advances in neural information processing systems, 753-760, 2004 | 138 | 2004 |
Sensorimotor Adaptation of Speech ICompensation and Adaptation JF Houde, MI Jordan Journal of Speech, Language, and Hearing Research 45 (2), 295-310, 2002 | 138 | 2002 |
Support union recovery in high-dimensional multivariate regression G Obozinski, MJ Wainwright, MI Jordan The Annals of Statistics, 1-47, 2011 | 136 | 2011 |
Perceptual distortion contributes to the curvature of human reaching movements DM Wolpert, Z Ghahramani, MI Jordan Experimental brain research 98 (1), 153-156, 1994 | 133 | 1994 |
Regression with input-dependent noise: A Gaussian process treatment PW Goldberg, CKI Williams, CM Bishop Advances in neural information processing systems 10, 493-499, 1997 | 132 | 1997 |
Statistical machine learning makes automatic control practical for internet datacenters P Bodık, R Griffith, C Sutton, A Fox, M Jordan, D Patterson Proceedings of the 2009 conference on Hot topics in cloud computing, 12-12, 2009 | 130 | 2009 |
Characterizing, modeling, and generating workload spikes for stateful services P Bodik, A Fox, MJ Franklin, MI Jordan, DA Patterson Proceedings of the 1st ACM symposium on Cloud computing, 241-252, 2010 | 128 | 2010 |
Statistical debugging: simultaneous identification of multiple bugs AX Zheng, MI Jordan, B Liblit, M Naik, A Aiken Proceedings of the 23rd international conference on Machine learning, 1105-1112, 2006 | 126 | 2006 |
Variational methods for the Dirichlet process DM Blei, MI Jordan Proceedings of the twenty-first international conference on Machine learning, 12, 2004 | 126 | 2004 |
Genome-wide requirements for resistance to functionally distinct DNA-damaging agents W Lee, RP St Onge, M Proctor, P Flaherty, MI Jordan, AP Arkin, RW Davis, ... PLoS Genet 1 (2), e24, 2005 | 125 | 2005 |
Denoising source separation J Särelä, H Valpola | 125 | 2004 |
Why the logistic function? A tutorial discussion on probabilities and neural networks MI Jordan Computational Cognitive Science Technical Report, 1995 | 125 | 1995 |
Semiparametric latent factor models M Seeger, YW Teh, M Jordan | 123 | 2005 |
A variational approach to Bayesian logistic regression models and their extensions T Jaakkola, MI Jordan Sixth International Workshop on Artificial Intelligence and Statistics, 1997 | 122 | 1997 |
Estimating divergence functionals and the likelihood ratio by convex risk minimization XL Nguyen, MJ Wainwright, M Jordan Information Theory, IEEE Transactions on 56 (11), 5847-5861, 2010 | 120 | 2010 |
Computational models of sensorimotor integration Z Ghahramani, DM Wolptrt, MI Jordan Advances in Psychology 119, 117-147, 1997 | 120 | 1997 |
A sticky HDP-HMM with application to speaker diarization EB Fox, EB Sudderth, MI Jordan, AS Willsky The Annals of Applied Statistics, 1020-1056, 2011 | 118 | 2011 |
Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization T Li, C Ding, M Jordan Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, 577-582, 2007 | 118 | 2007 |
Variational methods for inference and estimation in graphical models TS Jaakkola, MI Jordan Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1997 | 118 | 1997 |
Thin junction trees FR Bach, MI Jordan Advances in Neural Information Processing Systems, 569-576, 2001 | 117 | 2001 |
Hidden Markov decision trees MI Jordany, Z Ghahramaniz, LK Sauly | 117 | 1997 |
Action. MI Jordan, DA Rosenbaum The MIT Press, 1989 | 111 | 1989 |
Shared segmentation of natural scenes using dependent Pitman-Yor processes EB Sudderth, MI Jordan Advances in Neural Information Processing Systems, 1585-1592, 2009 | 110 | 2009 |
Boltzmann chains and hidden Markov models LK Saul, MI Jordan Advances in neural information processing systems, 435-442, 1995 | 110 | 1995 |
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators P Liang, MI Jordan Proceedings of the 25th international conference on Machine learning, 584-591, 2008 | 107 | 2008 |
Computing regularization paths for learning multiple kernels FR Bach, R Thibaux, MI Jordan Advances in neural information processing systems 17, 73-80, 2005 | 105 | 2005 |
Communication-efficient online detection of network-wide anomalies L Huang, XL Nguyen, M Garofalakis, JM Hellerstein, M Jordan, ... INFOCOM 2007. 26th IEEE International Conference on Computer Communications ..., 2007 | 102 | 2007 |
Minimax probability machine G Lanckriet, LE Ghaoui, C Bhattacharyya, MI Jordan Advances in neural information processing systems, 801-807, 2001 | 102 | 2001 |
Beyond independent components: trees and clusters FR Bach, MI Jordan The Journal of Machine Learning Research 4, 1205-1233, 2003 | 101 | 2003 |
An introduction to linear algebra in parallel distributed processing MI Jordan Parallel distributed processing 1, 365-422, 1986 | 101 | 1986 |
Structured prediction, dual extragradient and Bregman projections B Taskar, S Lacoste-Julien, MI Jordan The Journal of Machine Learning Research 7, 1627-1653, 2006 | 100 | 2006 |
Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization P Bodik, G Friedman, L Biewald, H Levine, G Candea, K Patel, G Tolle, ... Autonomic Computing, 2005. ICAC 2005. Proceedings. Second International ..., 2005 | 100 | 2005 |
An introduction to graphical models MI Jordan, C Bishop progress, 2004 | 100 | 2004 |
Learning semantic correspondences with less supervision P Liang, MI Jordan, D Klein Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL ..., 2009 | 98 | 2009 |
Obstacle avoidance and a perturbation sensitivity model for motor planning PN Sabes, MI Jordan The Journal of Neuroscience 17 (18), 7119-7128, 1997 | 98 | 1997 |
Bayesian haplotype inference via the Dirichlet process EP Xing, MI Jordan, R Sharan Journal of Computational Biology 14 (3), 267-284, 2007 | 97 | 2007 |
In-network PCA and anomaly detection L Huang, XL Nguyen, M Garofalakis, MI Jordan, A Joseph, N Taft Advances in Neural Information Processing Systems, 617-624, 2006 | 97 | 2006 |
Graphical models: Probabilistic inference MI Jordan, Y Weiss The Handbook of Brain Theory and Neural Networks,, 2002 | 97 | 2002 |
Support vector machines for analog circuit performance representation F De Bernardinis, MI Jordan, A SangiovanniVincentelli Design Automation Conference, 2003. Proceedings, 964-969, 2003 | 96 | 2003 |
Revisiting k-means: New algorithms via Bayesian nonparametrics B Kulis, MI Jordan arXiv preprint arXiv:1111.0352, 2011 | 95 | 2011 |
Blind one-microphone speech separation: A spectral learning approach FRBMI Jordan Advances in Neural Information Processing Systems 17: Proceedings of the ..., 2005 | 92 | 2005 |
Tree-structured stick breaking for hierarchical data Z Ghahramani, MI Jordan, RP Adams Advances in neural information processing systems, 19-27, 2010 | 89 | 2010 |
Computational consequences of a bias toward short connections RA Jacobs, MI Jordan Journal of cognitive neuroscience 4 (4), 323-336, 1992 | 86 | 1992 |
The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements. B Trushkowsky, P Bodík, A Fox, MJ Franklin, MI Jordan, DA Patterson FAST, 163-176, 2011 | 85 | 2011 |
Graphical models: Foundations of neural computation MI Jordan MIT press, 2001 | 85 | 2001 |
The role of inertial sensitivity in motor planning PN Sabes, MI Jordan, DM Wolpert The Journal of neuroscience 18 (15), 5948-5957, 1998 | 85 | 1998 |
Robust novelty detection with single-class MPM LE Ghaoui, MI Jordan, GR Lanckriet Advances in neural information processing systems, 905-912, 2002 | 84 | 2002 |
Improving the mean field approximation via the use of mixture distributions TS Jaakkola, MI Jordan Learning in graphical models, 163-173, 1998 | 84 | 1998 |
Viewing the hand prior to movement improves accuracy of pointing performed toward the unseen contralateral hand M Desmurget, Y Rossetti, M Jordan, C Meckler, C Prablanc Experimental Brain Research 115 (1), 180-186, 1997 | 83 | 1997 |
MLbase: A Distributed Machine-learning System. T Kraska, A Talwalkar, JC Duchi, R Griffith, MJ Franklin, MI Jordan CIDR, 2013 | 81 | 2013 |
Nonparametric Bayes applications to biostatistics DB Dunson Bayesian nonparametrics 28, 223, 2010 | 79 | 2010 |
Local linear perceptrons for classification E Alpaydin, M Jordan Neural Networks, IEEE Transactions on 7 (3), 788-794, 1996 | 79 | 1996 |
Computing upper and lower bounds on likelihoods in intractable networks TS Jaakkola, MI Jordan Proceedings of the Twelfth international conference on Uncertainty in ..., 1996 | 78 | 1996 |
Word alignment via quadratic assignment S Lacoste-Julien, B Taskar, D Klein, MI Jordan Proceedings of the main conference on Human Language Technology Conference ..., 2006 | 77 | 2006 |
Nonparametric empirical Bayes for the Dirichlet process mixture model JD McAuliffe, DM Blei, MI Jordan Statistics and Computing 16 (1), 5-14, 2006 | 77 | 2006 |
Nonparametric Bayesian learning of switching linear dynamical systems EB Fox, EB Sudderth, MI Jordan, AS Willsky Advances in Neural Information Processing Systems, 457-464, 2009 | 76 | 2009 |
Learning multiscale representations of natural scenes using Dirichlet processes JJ Kivinen, EB Sudderth, M Jordan Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 1-8, 2007 | 76 | 2007 |
Divide-and-conquer matrix factorization LW Mackey, MI Jordan, A Talwalkar Advances in Neural Information Processing Systems, 1134-1142, 2011 | 75 | 2011 |
Sharing features among dynamical systems with beta processes EB Fox, MI Jordan, EB Sudderth, AS Willsky Advances in Neural Information Processing Systems, 549-557, 2009 | 75 | 2009 |
Constrained supervised learning MI Jordan Journal of Mathematical Psychology 36 (3), 396-425, 1992 | 72 | 1992 |
Learning from measurements in exponential families P Liang, MI Jordan, D Klein Proceedings of the 26th annual international conference on machine learning ..., 2009 | 71 | 2009 |
Nonparametric decentralized detection using kernel methods XL Nguyen, MJ Wainwright, M Jordan Signal Processing, IEEE Transactions on 53 (11), 4053-4066, 2005 | 71 | 2005 |
Logos: a modular bayesian model for de novo motif detection EP Xing, W Wu, MI Jordan, RM Karp Journal of Bioinformatics and Computational Biology 2 (01), 127-154, 2004 | 70 | 2004 |
Computational and statistical tradeoffs via convex relaxation V Chandrasekaran, MI Jordan Proceedings of the National Academy of Sciences 110 (13), E1181-E1190, 2013 | 69 | 2013 |
Genomic privacy and limits of individual detection in a pool S Sankararaman, G Obozinski, MI Jordan, E Halperin Nature genetics 41 (9), 965-967, 2009 | 69 | 2009 |
Nonnegative matrix factorization for combinatorial optimization: Spectral clustering, graph matching, and clique finding C Ding, T Li, M Jordan Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, 183-192, 2008 | 69 | 2008 |
On semidefinite relaxations for normalized k-cut and connections to spectral clustering E Xing, EP Xing, M Jordan, MI Jordan | 69 | 2003 |
Goal-based speech motor control: A theoretical framework and some preliminary data JS Perkell, ML Matthies, MA Svirsky, MI Jordan Journal of Phonetics 23 (1), 23-35, 1995 | 69 | 1995 |
A model of the learning of arm trajectories from spatial deviations MI Jordan, T Flash, Y Arnon Journal of Cognitive Neuroscience 6 (4), 359-376, 1994 | 69 | 1994 |
Consistent probabilistic outputs for protein function prediction G Obozinski, G Lanckriet, C Grant, MI Jordan, WS Noble Genome Biology 9 (Suppl 1), S6, 2008 | 68 | 2008 |
Sulfur and nitrogen limitation in Escherichia coli K-12: specific homeostatic responses P Gyaneshwar, O Paliy, J McAuliffe, DL Popham, MI Jordan, S Kustu Journal of bacteriology 187 (3), 1074-1090, 2005 | 67 | 2005 |
A minimal intervention principle for coordinated movement E Todorov, MI Jordan Advances in neural information processing systems, 27-34, 2002 | 67 | 2002 |
Efficient stepwise selection in decomposable models A Deshpande, M Garofalakis, MI Jordan Proceedings of the Seventeenth conference on Uncertainty in artificial ..., 2001 | 67 | 2001 |
Mining Console Logs for Large-Scale System Problem Detection. W Xu, L Huang, A Fox, DA Patterson, MI Jordan SysML 8, 4-4, 2008 | 66 | 2008 |
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny JD McAuliffe, L Pachter, MI Jordan Bioinformatics 20 (12), 1850-1860, 2004 | 66 | 2004 |
Modular and hierarchical learning systems MI Jordan, RA Jacobs The handbook of brain theory and neural networks, 579-582, 1995 | 65 | 1995 |
Bayesian nonparametric inference of switching dynamic linear models E Fox, E Sudderth, M Jordan, A Willsky Signal Processing, IEEE Transactions on 59 (4), 1569-1585, 2011 | 63 | 2011 |
A scalable bootstrap for massive data A Kleiner, A Talwalkar, P Sarkar, MI Jordan Journal of the Royal Statistical Society: Series B (Statistical Methodology ..., 2014 | 62 | 2014 |
Multiple non-redundant spectral clustering views D Niu, JG Dy, MI Jordan Proceedings of the 27th international conference on machine learning (ICML ..., 2010 | 62 | 2010 |
Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data C Bhattacharyya, LR Grate, A Rizki, D Radisky, FJ Molina, MI Jordan, ... Signal Processing 83 (4), 729-743, 2003 | 62 | 2003 |
Statistical debugging of sampled programs AX Zheng, MI Jordan, B Liblit, A Aiken Advances in Neural Information Processing Systems, None, 2003 | 61 | 2003 |
Learning graphical models with Mercer kernels FR Bach, MI Jordan Advances in Neural Information Processing Systems, 1009-1016, 2002 | 61 | 2002 |
Asymptotic convergence rate of the EM algorithm for Gaussian mixtures J Ma, L Xu, MI Jordan Neural Computation 12 (12), 2881-2907, 2000 | 61 | 2000 |
The organization of action sequences: Evidence from a relearning task MI Jordan Journal of Motor Behavior 27 (2), 179-192, 1995 | 61 | 1995 |
Structured prediction via the extragradient method B Taskar, S Lacoste-Julien, M Jordan NIPS, 2005 | 60 | 2005 |
A latent variable model for chemogenomic profiling P Flaherty, G Giaever, J Kumm, MI Jordan, AP Arkin Bioinformatics 21 (15), 3286-3293, 2005 | 56 | 2005 |
Approximating posterior distributions in belief networks using mixtures CMBN Lawrence, TJMI Jordan Advances in Neural Information Processing Systems 10: Proceedings of the ..., 1998 | 56 | 1998 |
Markov mixtures of experts M Meila, MI Jordan Multiple Model Approaches to Modelling and Control, 145-166, 1996 | 56 | 1996 |
Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model D Ting, G Wang, M Shapovalov, R Mitra, MI Jordan, RL Dunbrack Jr PLoS Comput Biol 6 (4), e1000763, 2010 | 55 | 2010 |