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Note: Links lead to the DBLP on the Web. Michael I. Jordan Eric P. Xing , Wei Wu , Michael I. Jordan, Richard M. Karp : LOGOS: a modular Bayesian model for de novo motif detection. CSB 2003 : 266-276 Ben Liblit , Alexander Aiken , Alice X. Zheng , Michael I. Jordan: Bug isolation via remote program sampling. PLDI 2003 : 141-154 David M. Blei , Michael I. Jordan: Modeling annotated data. SIGIR 2003 : 127-134 Eric P. Xing , Michael I. Jordan, Stuart Russell : A generalized mean field algorithm for variational inference in exponential families. UAI 2003 : 583-591 Kobus Barnard , Pinar Duygulu , David A. Forsyth , Nando de Freitas , David M. Blei , Michael I. Jordan: Matching Words and Pictures. Journal of Machine Learning Research 3 : 1107-1135 (2003) David M. Blei , Andrew Y. Ng , Michael I. Jordan: Latent Dirichlet Allocation. Journal of Machine Learning Research 3 : 993-1022 (2003) Francis R. Bach , Michael I. Jordan: Beyond Independent Components: Trees and Clusters. Journal of Machine Learning Research 4 : 1205-1233 (2003) Gert R. G. Lanckriet , Nello Cristianini , Peter L. Bartlett , Laurent El Ghaoui , Michael I. Jordan: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5 : 27-72 (2003) Kenji Fukumizu , Francis R. Bach , Michael I. Jordan: Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. Journal of Machine Learning Research 5 : 73-99 (2003) Christophe Andrieu , Nando de Freitas , Arnaud Doucet , Michael I. Jordan: An Introduction to MCMC for Machine Learning. Machine Learning 50 (1-2): 5-43 (2003) Gert R. G. Lanckriet , Nello Cristianini , Peter L. Bartlett , Laurent El Ghaoui , Michael I. Jordan: Learning the Kernel Matrix with Semi-Definite Programming. ICML 2002 : 323-330 Francis R. Bach , Michael I. Jordan: Tree-dependent Component Analysis. UAI 2002 : 36-44 Sekhar Tatikonda , Michael I. Jordan: Loopy Belief Propogation and Gibbs Measures. UAI 2002 : 493-500 L. R. Grate , Chiranjib Bhattacharyya , Michael I. Jordan, I. Saira Mian : Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. WABI 2002 : 1-9 Francis R. Bach , Michael I. Jordan: Kernel Independent Component Analysis. Journal of Machine Learning Research 3 : 1-48 (2002) Gert R. G. Lanckriet , Laurent El Ghaoui , Chiranjib Bhattacharyya , Michael I. Jordan: A Robust Minimax Approach to Classification. Journal of Machine Learning Research 3 : 555-582 (2002) Michael I. Jordan, Terrence J. Sejnowski : Graphical Models: Foundations of Neural Computation. Pattern Anal. Appl. 5 (4): 401-402 (2002) Andrew Y. Ng , Michael I. Jordan: Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001 : 377-384 Eric P. Xing , Michael I. Jordan, Richard M. Karp : Feature selection for high-dimensional genomic microarray data. ICML 2001 : 601-608 Andrew Y. Ng , Alice X. Zheng , Michael I. Jordan: Link Analysis, Eigenvectors and Stability. IJCAI 2001 : 903-910 F. R. Bach , Michael I. Jordan: Thin Junction Trees. NIPS 2001 : 569-576 David M. Blei , A. Y. Ng , Michael I. Jordan: Latent Dirichlet Allocation. NIPS 2001 : 601-608 Gert R. G. Lanckriet , Laurent E. Ghaoui , Chiranjib Bhattacharyya , Michael I. Jordan: Minimax Probability Machine. NIPS 2001 : 801-807 Andrew Y. Ng , Michael I. Jordan: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001 : 841-848 Andrew Y. Ng , Michael I. Jordan, Y. Weiss : On Spectral Clustering: Analysis and an algorithm. NIPS 2001 : 849-856 Alice X. Zheng , Andrew Y. Ng , Michael I. Jordan: Stable Algorithms for Link Analysis. SIGIR 2001 : 258-266 Amol Deshpande , Minos N. Garofalakis , Michael I. Jordan: Efficient Stepwise Selection in Decomposable Models. UAI 2001 : 128-135 Jinwen Ma , Lei Xu , Michael I. Jordan: Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. Neural Computation 12 (12): 2881-2907 (2001) Andrew Y. Ng , Michael I. Jordan: PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000 : 406-415 Marina Meila , Michael I. Jordan: Learning with Mixtures of Trees. Journal of Machine Learning Research 1 : 1-48 (2000) Lawrence K. Saul , Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12 (6): 1313-1335 (2000) Andrew Y. Ng , Michael I. Jordan: Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999 : 533-539 Kevin P. Murphy , Yair Weiss , Michael I. Jordan: Loopy Belief Propagation for Approximate Inference: An Empirical Study. UAI 1999 : 467-475 Tommi Jaakkola , Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. JAIR 10 : 291-322 (1999) Lawrence K. Saul , Michael I. Jordan: Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Machine Learning 37 (1): 75-87 (1999) 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) Michael I. Jordan, Michael J. Kearns , Sara A. Solla : Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997] The MIT Press 1998 Thomas Hofmann , Jan Puzicha , Michael I. Jordan: Learning from Dyadic Data. NIPS 1998 : 466-472 Neil D. Lawrence , Christopher M. Bishop , Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998 : 320-327 Michael Mozer , Michael I. Jordan, Thomas Petsche : Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996 MIT Press 1997 John F. Houde , Michael I. Jordan: Adaptation in Speech Motor Control. NIPS 1997 Christopher M. Bishop , Neil D. Lawrence , Tommi Jaakkola , Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997 Marina Meila , Michael I. Jordan: Estimating Dependency Structure as a Hidden Variable. NIPS 1997 Michael I. Jordan, Christopher M. Bishop : Neural Networks. The Computer Science and Engineering Handbook 1997 : 536-556 Zoubin Ghahramani , Michael I. Jordan: Factorial Hidden Markov Models. Machine Learning 29 (2-3): 245-273 (1997) Padhraic Smyth , David Heckerman , Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9 (2): 227-269 (1997) Lawrence K. Saul , Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996 : 267-273 Tommi Jaakkola , Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996 : 487-493 Michael I. Jordan, Zoubin Ghahramani , Lawrence K. Saul : Hidden Markov Decision Trees. NIPS 1996 : 501-507 Marina Meila , Michael I. Jordan: Triangulation by Continuous Embedding. NIPS 1996 : 557-563 Tommi Jaakkola , Michael I. Jordan: Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996 : 340-348 Michael I. Jordan, Christopher M. Bishop : Neural Networks. ACM Comput. Surv. 28 (1): 73-75 (1996) David A. Cohn , Zoubin Ghahramani , Michael I. Jordan: Active Learning with Statistical Models. JAIR 4 : 129-145 (1996) Lawrence K. Saul , Tommi Jaakkola , Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. JAIR 4 : 61-76 (1996) Marina Meila , Michael I. Jordan: Learning Fine Motion by Markov Mixtures of Experts. NIPS 1995 : 1003-1009 Philip N. Sabes , Michael I. Jordan: Reinforcement Learning by Probability Matching. NIPS 1995 : 1080-1086 Zoubin Ghahramani , Michael I. Jordan: Factorial Hidden Markov Models. NIPS 1995 : 472-478 Lawrence K. Saul , Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995 : 486-492 Tommi Jaakkola , Lawrence K. Saul , Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995 : 528-534 Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. COLT 1994 : 13-20 Satinder P. Singh , Tommi Jaakkola , Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994 : 284-292 Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. ICML 1994 : 363-370 Zoubin Ghahramani , Daniel M. Wolpert , Michael I. Jordan: Computational Structure of coordinate transformations: A generalization study. NIPS 1994 : 1125-1132 Tommi Jaakkola , Satinder P. Singh , Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994 : 345-352 Satinder P. Singh , Tommi Jaakkola , Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994 : 361-368 Daniel M. Wolpert , Zoubin Ghahramani , Michael I. Jordan: Forward dynamic models in human motor control: Psychophysical evidence. NIPS 1994 : 43-50 Lawrence K. Saul , Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994 : 435-442 Lei Xu , Michael I. Jordan, Geoffrey E. Hinton : An Alternative Model for Mixtures of Experts. NIPS 1994 : 633-640 David A. Cohn , Zoubin Ghahramani , Michael I. Jordan: Active Learning with Statistical Models. NIPS 1994 : 705-712 Michael I. Jordan, Robert A. Jacobs : Supervised Learning and Divide-and-Conquer: A Statistical Approach. ICML 1993 : 159-166 Robert A. Jacobs , Michael I. Jordan, Andrew G. Barto : Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Machine Learning: From Theory to Applications 1993 : 175-202 Zoubin Ghahramani , Michael I. Jordan: Supervised learning from incomplete data via an EM approach. NIPS 1993 : 120-127 Tommi Jaakkola , Michael I. Jordan, Satinder P. Singh : Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993 : 703-710 Daphne Bavelier , Michael I. Jordan: A Dynamical Model of Priming and Repetition Blindness. NIPS 1992 : 879-886 Michael I. Jordan, David E. Rumelhart : Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16 (3): 307-354 (1992) Michael I. Jordan, David E. Rumelhart : Internal World Models and Supervised Learning. ML 1991 : 70-74 Makuto Hirayama , Eric Vatikiotis-Bateson , Mitsuo Kawato , Michael I. Jordan: Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. NIPS 1991 : 191-198 Michael I. Jordan, Robert A. Jacobs : Hierarchies of Adaptive Experts. NIPS 1991 : 985-992 Robert A. Jacobs , Michael I. Jordan, Andrew G. Barto : Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cognitive Science 15 (2): 219-250 (1991) Robert A. Jacobs , Michael I. Jordan: A Competitive Modular Connectionist Architecture. NIPS 1990 : 767-773 Michael I. Jordan, Robert A. Jacobs : Learning to Control an Unstable System with Forward Modeling. NIPS 1989 : 324-331 1 [ 80 ] 2 [ 72 ] 3 [ 61 ] 4 [ 67 ] [ 70 ] [ 73 ] [ 75 ] 5 [ 77 ] 6 [ 71 ] [ 74 ] 7 [ 3 ] [ 11 ] 8 [ 8 ] 9 [ 59 ] [ 66 ] [ 68 ] 10 [ 30 ] [ 38 ] [ 40 ] [ 43 ] 11 [ 60 ] [ 76 ] [ 77 ] [ 79 ] 12 [ 13 ] [ 29 ] 13 [ 71 ] [ 74 ] 14 [ 55 ] 15 [ 72 ] 16 [ 77 ] 17 [ 77 ] 18 [ 72 ] [ 77 ] 19 [ 73 ] 20 [ 55 ] 21 [ 10 ] [ 13 ] [ 16 ] [ 19 ] [ 25 ] [ 29 ] [ 33 ] [ 37 ] [ 46 ] 22 [ 59 ] 23 [ 66 ] [ 71 ] [ 74 ] 24 [ 68 ] 25 [ 36 ] 26 [ 14 ] 27 [ 5 ] 28 [ 44 ] 29 [ 41 ] 30 [ 9 ] [ 17 ] [ 18 ] [ 21 ] [ 23 ] [ 28 ] [ 31 ] [ 34 ] [ 40 ] [ 46 ] [ 48 ] 31 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 11 ] [ 12 ] 32 [ 63 ] [ 81 ] 33 [ 5 ] 34 [ 45 ] 35 [ 59 ] [ 66 ] [ 71 ] [ 74 ] 36 [ 40 ] [ 43 ] 37 [ 80 ] 38 [ 54 ] 39 [ 27 ] [ 32 ] [ 39 ] [ 52 ] 40 [ 68 ] 41 [ 42 ] 42 [ 49 ] 43 [ 60 ] 44 [ 50 ] [ 53 ] [ 56 ] [ 57 ] [ 58 ] [ 62 ] [ 64 ] [ 76 ] 45 [ 42 ] 46 [ 44 ] 47 [ 6 ] [ 7 ] 48 [ 78 ] 49 [ 26 ] 50 [ 15 ] [ 23 ] [ 24 ] [ 28 ] [ 33 ] [ 35 ] [ 46 ] [ 47 ] [ 51 ] 51 [ 65 ] 52 [ 9 ] [ 17 ] [ 18 ] [ 21 ] 53 [ 36 ] 54 [ 45 ] 55 [ 69 ] 56 [ 5 ] 57 [ 57 ] 58 [ 49 ] 59 [ 16 ] [ 19 ] 60 [ 81 ] 61 [ 63 ] [ 78 ] [ 81 ] 62 [ 14 ] [ 54 ] 63 [ 56 ] [ 62 ] [ 80 ]  ©2004 Association for Computing Machinery |
