 | 2010 |
|---|
| 180 |       | Charles A. Sutton,
Michael I. Jordan:
Bayesian Inference in Queueing Networks
CoRR abs/1001.3355: (2010) |
| 179 | | David M. Blei,
Thomas L. Griffiths,
Michael I. Jordan:
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies.
J. ACM 57(2): (2010) |
| 2009 |
|---|
| 178 | | Zhihua Zhang,
Guang Dai,
Michael I. Jordan:
A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis.
ECML/PKDD (2) 2009: 632-647 |
| 177 | | Archana Ganapathi,
Harumi A. Kuno,
Umeshwar Dayal,
Janet L. Wiener,
Armando Fox,
Michael I. Jordan,
David A. Patterson:
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning.
ICDE 2009: 592-603 |
| 176 | | Wei Xu,
Ling Huang,
Armando Fox,
David Patterson,
Michael I. Jordan:
Online System Problem Detection by Mining Patterns of Console Logs.
ICDM 2009: 588-597 |
| 175 | | Percy Liang,
Michael I. Jordan,
Dan Klein:
Learning from measurements in exponential families.
ICML 2009: 81 |
| 174 | | Donghui Yan,
Ling Huang,
Michael I. Jordan:
Fast approximate spectral clustering.
KDD 2009: 907-916 |
| 173 | | Michael I. Jordan:
Combinatorial stochastic processes and nonparametric Bayesian modeling.
SODA 2009: 139 |
| 172 | | Wei Xu,
Ling Huang,
Armando Fox,
David A. Patterson,
Michael I. Jordan:
Detecting large-scale system problems by mining console logs.
SOSP 2009: 117-132 |
| 171 | | Junming Yin,
Michael I. Jordan,
Yun S. Song:
Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
Bioinformatics 25(12): (2009) |
| 2008 |
|---|
| 170 | | Chris H. Q. Ding,
Tao Li,
Michael I. Jordan:
Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding.
ICDM 2008: 183-192 |
| 169 | | Emily B. Fox,
Erik B. Sudderth,
Michael I. Jordan,
Alan S. Willsky:
An HDP-HMM for systems with state persistence.
ICML 2008: 312-319 |
| 168 | | Percy Liang,
Michael I. Jordan:
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators.
ICML 2008: 584-591 |
| 167 | | Guillaume Obozinski,
Martin J. Wainwright,
Michael I. Jordan:
High-dimensional support union recovery in multivariate regression.
NIPS 2008: 1217-1224 |
| 166 | | Erik B. Sudderth,
Michael I. Jordan:
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes.
NIPS 2008: 1585-1592 |
| 165 | | Alexandre Bouchard-Côté,
Michael I. Jordan,
Dan Klein:
Efficient Inference in Phylogenetic InDel Trees.
NIPS 2008: 177-184 |
| 164 | | Zhihua Zhang,
Michael I. Jordan,
Dit-Yan Yeung:
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice.
NIPS 2008: 1969-1976 |
| 163 | | Emily B. Fox,
Erik B. Sudderth,
Michael I. Jordan,
Alan S. Willsky:
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems.
NIPS 2008: 457-464 |
| 162 | | Ling Huang,
Donghui Yan,
Michael I. Jordan,
Nina Taft:
Spectral Clustering with Perturbed Data.
NIPS 2008: 705-712 |
| 161 | | Simon Lacoste-Julien,
Fei Sha,
Michael I. Jordan:
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification.
NIPS 2008: 897-904 |
| 160 | | Sriram Sankararaman,
Gad Kimmel,
Eran Halperin,
Michael I. Jordan:
On the Inference of Ancestries in Admixed Populations.
RECOMB 2008: 424-433 |
| 159 | | Wei Xu,
Ling Huang,
Armando Fox,
David A. Patterson,
Michael I. Jordan:
Mining Console Logs for Large-Scale System Problem Detection.
SysML 2008 |
| 158 | | Charles A. Sutton,
Michael I. Jordan:
Probabilistic Inference in Queueing Networks.
SysML 2008 |
| 157 | | Kurt T. Miller,
Thomas L. Griffiths,
Michael I. Jordan:
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features.
UAI 2008: 403-410 |
| 156 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
Estimating divergence functionals and the likelihood ratio by convex risk minimization
CoRR abs/0809.0853: (2008) |
| 155 | | Martin J. Wainwright,
Michael I. Jordan:
Graphical Models, Exponential Families, and Variational Inference.
Foundations and Trends in Machine Learning 1(1-2): 1-305 (2008) |
| 154 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection.
IEEE Transactions on Information Theory 54(7): 3285-3295 (2008) |
| 2007 |
|---|
| 153 | | Michael I. Jordan:
Statistical Machine Learning and Computational Biology.
BIBM 2007: 4 |
| 152 | | Jyri J. Kivinen,
Erik B. Sudderth,
Michael I. Jordan:
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes.
ICCV 2007: 1-8 |
| 151 | | Tao Li,
Chris H. Q. Ding,
Michael I. Jordan:
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization.
ICDM 2007: 577-582 |
| 150 | | Jyri J. Kivinen,
Erik B. Sudderth,
Michael I. Jordan:
Image Denoising with Nonparametric Hidden Markov Trees.
ICIP (3) 2007: 121-124 |
| 149 | | Percy Liang,
Michael I. Jordan,
Benjamin Taskar:
A permutation-augmented sampler for DP mixture models.
ICML 2007: 545-552 |
| 148 | | Jens Nilsson,
Fei Sha,
Michael I. Jordan:
Regression on manifolds using kernel dimension reduction.
ICML 2007: 697-704 |
| 147 | | Ling Huang,
XuanLong Nguyen,
Minos N. Garofalakis,
Joseph M. Hellerstein,
Michael I. Jordan,
Anthony D. Joseph,
Nina Taft:
Communication-Efficient Online Detection of Network-Wide Anomalies.
INFOCOM 2007: 134-142 |
| 146 | | Percy Liang,
Dan Klein,
Michael I. Jordan:
Agreement-Based Learning.
NIPS 2007 |
| 145 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
NIPS 2007 |
| 144 | | Ben Blum,
Michael I. Jordan,
David Kim,
Rhiju Das,
Philip Bradley,
David Baker:
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
NIPS 2007 |
| 143 | | Eric P. Xing,
Michael I. Jordan,
Roded Sharan:
Bayesian Haplotype Inference via the Dirichlet Process.
Journal of Computational Biology 14(3): 267-284 (2007) |
| 2006 |
|---|
| 142 | | Simon Lacoste-Julien,
Benjamin Taskar,
Dan Klein,
Michael I. Jordan:
Word Alignment via Quadratic Assignment.
HLT-NAACL 2006 |
| 141 | | Eric P. Xing,
Kyung-Ah Sohn,
Michael I. Jordan,
Yee Whye Teh:
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture.
ICML 2006: 1049-1056 |
| 140 | | Alice X. Zheng,
Michael I. Jordan,
Ben Liblit,
Mayur Naik,
Alex Aiken:
Statistical debugging: simultaneous identification of multiple bugs.
ICML 2006: 1105-1112 |
| 139 | | Barbara E. Engelhardt,
Michael I. Jordan,
Steven E. Brenner:
A graphical model for predicting protein molecular function.
ICML 2006: 297-304 |
| 138 | | Ling Huang,
XuanLong Nguyen,
Minos N. Garofalakis,
Michael I. Jordan,
Anthony D. Joseph,
Nina Taft:
In-Network PCA and Anomaly Detection.
NIPS 2006: 617-624 |
| 137 | | Zhihua Zhang,
Michael I. Jordan:
Bayesian Multicategory Support Vector Machines.
UAI 2006 |
| 136 | | David M. Blei,
K. Franks,
Michael I. Jordan,
I. Saira Mian:
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span.
BMC Bioinformatics 7: 250 (2006) |
| 135 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
On optimal quantization rules for some sequential decision problems
CoRR abs/math/0608556: (2006) |
| 134 | | Martin J. Wainwright,
Michael I. Jordan:
Log-determinant relaxation for approximate inference in discrete Markov random fields.
IEEE Transactions on Signal Processing 54(6-1): 2099-2109 (2006) |
| 133 | | Benjamin Taskar,
Simon Lacoste-Julien,
Michael I. Jordan:
Structured Prediction, Dual Extragradient and Bregman Projections.
Journal of Machine Learning Research 7: 1627-1653 (2006) |
| 132 | | Francis R. Bach,
Michael I. Jordan:
Learning Spectral Clustering, With Application To Speech Separation.
Journal of Machine Learning Research 7: 1963-2001 (2006) |
| 131 | | Jon D. McAuliffe,
David M. Blei,
Michael I. Jordan:
Nonparametric empirical Bayes for the Dirichlet process mixture model.
Statistics and Computing 16(1): 5-14 (2006) |
| 2005 |
|---|
| 130 | | Peter Bodík,
Greg Friedman,
Lukas Biewald,
Helen Levine,
George Candea,
Kayur Patel,
Gilman Tolle,
Jonathan Hui,
Armando Fox,
Michael I. Jordan,
David A. Patterson:
Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization.
ICAC 2005: 89-100 |
| 129 | | Francis R. Bach,
Michael I. Jordan:
Predictive low-rank decomposition for kernel methods.
ICML 2005: 33-40 |
| 128 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
Divergences, surrogate loss functions and experimental design.
NIPS 2005 |
| 127 | | Patrick Flaherty,
Michael I. Jordan,
Adam P. Arkin:
Robust design of biological experiments.
NIPS 2005 |
| 126 | | Benjamin Taskar,
Simon Lacoste-Julien,
Michael I. Jordan:
Structured Prediction via the Extragradient Method.
NIPS 2005 |
| 125 | | Ben Liblit,
Mayur Naik,
Alice X. Zheng,
Alexander Aiken,
Michael I. Jordan:
Scalable statistical bug isolation.
PLDI 2005: 15-26 |
| 124 | | Michal Rosen-Zvi,
Michael I. Jordan,
Alan L. Yuille:
The DLR Hierarchy of Approximate Inference.
UAI 2005: 493-500 |
| 123 | | Patrick Flaherty,
Guri Giaever,
Jochen Kumm,
Michael I. Jordan,
Adam P. Arkin:
A latent variable model for chemogenomic profiling.
Bioinformatics 21(15): 3286-3293 (2005) |
| 122 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
On divergences, surrogate loss functions, and decentralized detection
CoRR abs/math/0510521: (2005) |
| 121 | | XuanLong Nguyen,
Michael I. Jordan,
Bruno Sinopoli:
A kernel-based learning approach to ad hoc sensor network localization.
TOSN 1(1): 134-152 (2005) |
| 2004 |
|---|
| 120 | | Neil D. Lawrence,
John C. Platt,
Michael I. Jordan:
Extensions of the Informative Vector Machine.
Deterministic and Statistical Methods in Machine Learning 2004: 56-87 |
| 119 | | Mike Y. Chen,
Alice X. Zheng,
Jim Lloyd,
Michael I. Jordan,
Eric A. Brewer:
Failure Diagnosis Using Decision Trees.
ICAC 2004: 36-43 |
| 118 | | Eric P. Xing,
Roded Sharan,
Michael I. Jordan:
Bayesian haplo-type inference via the dirichlet process.
ICML 2004 |
| 117 | | XuanLong Nguyen,
Martin J. Wainwright,
Michael I. Jordan:
Decentralized detection and classification using kernel methods.
ICML 2004 |
| 116 | | Francis R. Bach,
Gert R. G. Lanckriet,
Michael I. Jordan:
Multiple kernel learning, conic duality, and the SMO algorithm.
ICML 2004 |
| 115 | | David M. Blei,
Michael I. Jordan:
Variational methods for the Dirichlet process.
ICML 2004 |
| 114 | | Alexandre d'Aspremont,
Laurent El Ghaoui,
Michael I. Jordan,
Gert R. G. Lanckriet:
A Direct Formulation for Sparse PCA Using Semidefinite Programming.
NIPS 2004 |
| 113 | | Francis R. Bach,
Michael I. Jordan:
Blind One-microphone Speech Separation: A Spectral Learning Approach.
NIPS 2004 |
| 112 | | Francis R. Bach,
Romain Thibaux,
Michael I. Jordan:
Computing regularization paths for learning multiple kernels.
NIPS 2004 |
| 111 | | Neil D. Lawrence,
Michael I. Jordan:
Semi-supervised Learning via Gaussian Processes.
NIPS 2004 |
| 110 | | Yee Whye Teh,
Michael I. Jordan,
Matthew J. Beal,
David M. Blei:
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
NIPS 2004 |
| 109 | | Gert R. G. Lanckriet,
Minghua Deng,
Nello Cristianini,
Michael I. Jordan,
William Stafford Noble:
Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast.
Pacific Symposium on Biocomputing 2004: 300-311 |
| 108 | | Eric P. Xing,
Michael I. Jordan:
Graph Partition Strategies for Generalized Mean Field Inference.
UAI 2004: 602-610 |
| 107 | | Jon D. McAuliffe,
Lior Pachter,
Michael I. Jordan:
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny.
Bioinformatics 20(12): 1850-1860 (2004) |
| 106 | | Gert R. G. Lanckriet,
Tijl De Bie,
Nello Cristianini,
Michael I. Jordan,
William Stafford Noble:
A statistical framework for genomic data fusion.
Bioinformatics 20(16): 2626-2635 (2004) |
| 105 | | Alexandre d'Aspremont,
Laurent El Ghaoui,
Michael I. Jordan,
Gert R. G. Lanckriet:
A direct formulation for sparse PCA using semidefinite programming
CoRR cs.CE/0406021: (2004) |
| 104 | | Eric P. Xing,
Wei Wu,
Michael I. Jordan,
Richard M. Karp:
Logos: a Modular Bayesian Model for de Novo Motif Detection.
J. Bioinformatics and Computational Biology 2(1): 127-154 (2004) |
| 103 | | Chiranjib Bhattacharyya,
L. R. Grate,
Michael I. Jordan,
Laurent El Ghaoui,
I. Saira Mian:
Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data.
Journal of Computational Biology 11(6): 1073-1089 (2004) |
| 102 | | 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 (2004) |
| 101 | | 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 (2004) |
| 2003 |
|---|
| 100 | | 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 |
| 99 | | Fernando De Bernardinis,
Michael I. Jordan,
Alberto L. Sangiovanni-Vincentelli:
Support vector machines for analog circuit performance representation.
DAC 2003: 964-969 |
| 98 | | Andrew Y. Ng,
H. Jin Kim,
Michael I. Jordan,
Shankar Sastry:
Autonomous Helicopter Flight via Reinforcement Learning.
NIPS 2003 |
| 97 | | David M. Blei,
Thomas L. Griffiths,
Michael I. Jordan,
Joshua B. Tenenbaum:
Hierarchical Topic Models and the Nested Chinese Restaurant Process.
NIPS 2003 |
| 96 | | Kenji Fukumizu,
Francis R. Bach,
Michael I. Jordan:
Kernel Dimensionality Reduction for Supervised Learning.
NIPS 2003 |
| 95 | | Peter L. Bartlett,
Michael I. Jordan,
Jon D. McAuliffe:
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.
NIPS 2003 |
| 94 | | Francis R. Bach,
Michael I. Jordan:
Learning Spectral Clustering.
NIPS 2003 |
| 93 | | XuanLong Nguyen,
Michael I. Jordan:
On the Concentration of Expectation and Approximate Inference in Layered Networks.
NIPS 2003 |
| 92 | | Martin J. Wainwright,
Michael I. Jordan:
Semidefinite Relaxations for Approximate Inference on Graphs with Cycles.
NIPS 2003 |
| 91 | | Alice X. Zheng,
Michael I. Jordan,
Ben Liblit,
Alexander Aiken:
Statistical Debugging of Sampled Programs.
NIPS 2003 |
| 90 | | Ben Liblit,
Alexander Aiken,
Alice X. Zheng,
Michael I. Jordan:
Bug isolation via remote program sampling.
PLDI 2003: 141-154 |
| 89 | | David M. Blei,
Michael I. Jordan:
Modeling annotated data.
SIGIR 2003: 127-134 |
| 88 |  | Eric P. Xing,
Michael I. Jordan,
Stuart J. Russell:
A generalized mean field algorithm for variational inference in exponential families.
UAI 2003: 583-591 |
| 87 | | 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) |
| 86 | | David M. Blei,
Andrew Y. Ng,
Michael I. Jordan:
Latent Dirichlet Allocation.
Journal of Machine Learning Research 3: 993-1022 (2003) |
| 85 | | Francis R. Bach,
Michael I. Jordan:
Beyond Independent Components: Trees and Clusters.
Journal of Machine Learning Research 4: 1205-1233 (2003) |
| 84 | | 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) |
| 83 | | Chiranjib Bhattacharyya,
L. R. Grate,
A. Rizki,
D. Radisky,
F. J. Molina,
Michael I. Jordan,
Mina J. Bissell,
I. Saira Mian:
Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data.
Signal Processing 83(4): 729-743 (2003) |
| 2002 |
|---|
| 82 | | 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 |
| 81 | | Francis R. Bach,
Michael I. Jordan:
Learning Graphical Models with Mercer Kernels.
NIPS 2002: 1009-1016 |
| 80 | | Eric P. Xing,
Michael I. Jordan,
Richard M. Karp,
Stuart J. Russell:
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences.
NIPS 2002: 1489-1496 |
| 79 | | Emanuel Todorov,
Michael I. Jordan:
A Minimal Intervention Principle for Coordinated Movement.
NIPS 2002: 27-34 |
| 78 | | Eric P. Xing,
Andrew Y. Ng,
Michael I. Jordan,
Stuart J. Russell:
Distance Metric Learning with Application to Clustering with Side-Information.
NIPS 2002: 505-512 |
| 77 | | Gert R. G. Lanckriet,
Laurent El Ghaoui,
Michael I. Jordan:
Robust Novelty Detection with Single-Class MPM.
NIPS 2002: 905-912 |
| 76 | | Francis R. Bach,
Michael I. Jordan:
Tree-dependent Component Analysis.
UAI 2002: 36-44 |
| 75 | | Sekhar Tatikonda,
Michael I. Jordan:
Loopy Belief Propogation and Gibbs Measures.
UAI 2002: 493-500 |
| 74 | | 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 |
| 73 | | Francis R. Bach,
Michael I. Jordan:
Kernel Independent Component Analysis.
Journal of Machine Learning Research 3: 1-48 (2002) |
| 72 | | 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) |
| 71 | | Michael I. Jordan,
Terrence J. Sejnowski:
Graphical Models: Foundations of Neural Computation.
Pattern Anal. Appl. 5(4): 401-402 (2002) |
| 2001 |
|---|
| 70 | | Andrew Y. Ng,
Michael I. Jordan:
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
ICML 2001: 377-384 |
| 69 | | Eric P. Xing,
Michael I. Jordan,
Richard M. Karp:
Feature selection for high-dimensional genomic microarray data.
ICML 2001: 601-608 |
| 68 | | Andrew Y. Ng,
Alice X. Zheng,
Michael I. Jordan:
Link Analysis, Eigenvectors and Stability.
IJCAI 2001: 903-910 |
| 67 | | Francis R. Bach,
Michael I. Jordan:
Thin Junction Trees.
NIPS 2001: 569-576 |
| 66 | | David M. Blei,
Andrew Y. Ng,
Michael I. Jordan:
Latent Dirichlet Allocation.
NIPS 2001: 601-608 |
| 65 | | Gert R. G. Lanckriet,
Laurent El Ghaoui,
Chiranjib Bhattacharyya,
Michael I. Jordan:
Minimax Probability Machine.
NIPS 2001: 801-807 |
| 64 | | Andrew Y. Ng,
Michael I. Jordan:
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes.
NIPS 2001: 841-848 |
| 63 | | Andrew Y. Ng,
Michael I. Jordan,
Yair Weiss:
On Spectral Clustering: Analysis and an algorithm.
NIPS 2001: 849-856 |
| 62 | | Alice X. Zheng,
Andrew Y. Ng,
Michael I. Jordan:
Stable Algorithms for Link Analysis.
SIGIR 2001: 258-266 |
| 61 | | Amol Deshpande,
Minos N. Garofalakis,
Michael I. Jordan:
Efficient Stepwise Selection in Decomposable Models.
UAI 2001: 128-135 |
| 60 | | Jinwen Ma,
Lei Xu,
Michael I. Jordan:
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures.
Neural Computation 12(12): 2881-2907 (2001) |
| 2000 |
|---|
| 59 | | Andrew Y. Ng,
Michael I. Jordan:
PEGASUS: A policy search method for large MDPs and POMDPs.
UAI 2000: 406-415 |
| 58 | | Marina Meila,
Michael I. Jordan:
Learning with Mixtures of Trees.
Journal of Machine Learning Research 1: 1-48 (2000) |
| 57 | | Lawrence K. Saul,
Michael I. Jordan:
Attractor Dynamics in Feedforward Neural Networks.
Neural Computation 12(6): 1313-1335 (2000) |
| 1999 |
|---|
| 56 | | Andrew Y. Ng,
Michael I. Jordan:
Approximate Inference A lgorithms for Two-Layer Bayesian Networks.
NIPS 1999: 533-539 |
| 55 | | Kevin P. Murphy,
Yair Weiss,
Michael I. Jordan:
Loopy Belief Propagation for Approximate Inference: An Empirical Study.
UAI 1999: 467-475 |
| 54 | | Tommi Jaakkola,
Michael I. Jordan:
Variational Probabilistic Inference and the QMR-DT Network.
J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999) |
| 53 | | 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) |
| 52 | | 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) |
| 1998 |
|---|
| 51 | | 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 |
| 50 | | Thomas Hofmann,
Jan Puzicha,
Michael I. Jordan:
Learning from Dyadic Data.
NIPS 1998: 466-472 |
| 49 | | Neil D. Lawrence,
Christopher M. Bishop,
Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines.
UAI 1998: 320-327 |
| 1997 |
|---|
| 48 | | 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 |
| 47 | | John F. Houde,
Michael I. Jordan:
Adaptation in Speech Motor Control.
NIPS 1997 |
| 46 | | Christopher M. Bishop,
Neil D. Lawrence,
Tommi Jaakkola,
Michael I. Jordan:
Approximating Posterior Distributions in Belief Networks Using Mixtures.
NIPS 1997 |
| 45 | | Marina Meila,
Michael I. Jordan:
Estimating Dependency Structure as a Hidden Variable.
NIPS 1997 |
| 44 | | Michael I. Jordan,
Christopher M. Bishop:
Neural Networks.
The Computer Science and Engineering Handbook 1997: 536-556 |
| 43 | | Zoubin Ghahramani,
Michael I. Jordan:
Factorial Hidden Markov Models.
Machine Learning 29(2-3): 245-273 (1997) |
| 42 | | Padhraic Smyth,
David Heckerman,
Michael I. Jordan:
Probabilistic Independence Networks for Hidden Markov Probability Models.
Neural Computation 9(2): 227-269 (1997) |
| 1996 |
|---|
| 41 | | Lawrence K. Saul,
Michael I. Jordan:
A Variational Principle for Model-based Morphing.
NIPS 1996: 267-273 |
| 40 | | Tommi Jaakkola,
Michael I. Jordan:
Recursive Algorithms for Approximating Probabilities in Graphical Models.
NIPS 1996: 487-493 |
| 39 | | Michael I. Jordan,
Zoubin Ghahramani,
Lawrence K. Saul:
Hidden Markov Decision Trees.
NIPS 1996: 501-507 |
| 38 | | Marina Meila,
Michael I. Jordan:
Triangulation by Continuous Embedding.
NIPS 1996: 557-563 |
| 37 | | Tommi Jaakkola,
Michael I. Jordan:
Computing upper and lower bounds on likelihoods in intractable networks.
UAI 1996: 340-348 |
| 36 | | Michael I. Jordan,
Christopher M. Bishop:
Neural Networks.
ACM Comput. Surv. 28(1): 73-75 (1996) |
| 35 | | Lawrence K. Saul,
Tommi Jaakkola,
Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks
CoRR cs.AI/9603102: (1996) |
| 34 | | David A. Cohn,
Zoubin Ghahramani,
Michael I. Jordan:
Active Learning with Statistical Models
CoRR cs.AI/9603104: (1996) |
| 33 | | David A. Cohn,
Zoubin Ghahramani,
Michael I. Jordan:
Active Learning with Statistical Models.
J. Artif. Intell. Res. (JAIR) 4: 129-145 (1996) |
| 32 | | Lawrence K. Saul,
Tommi Jaakkola,
Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks.
J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996) |
| 1995 |
|---|
| 31 | | Marina Meila,
Michael I. Jordan:
Learning Fine Motion by Markov Mixtures of Experts.
NIPS 1995: 1003-1009 |
| 30 | | Philip N. Sabes,
Michael I. Jordan:
Reinforcement Learning by Probability Matching.
NIPS 1995: 1080-1086 |
| 29 | | Zoubin Ghahramani,
Michael I. Jordan:
Factorial Hidden Markov Models.
NIPS 1995: 472-478 |
| 28 | | Lawrence K. Saul,
Michael I. Jordan:
Exploiting Tractable Substructures in Intractable Networks.
NIPS 1995: 486-492 |
| 27 | | Tommi Jaakkola,
Lawrence K. Saul,
Michael I. Jordan:
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks.
NIPS 1995: 528-534 |
| 26 | | Michael I. Jordan,
Lei Xu:
Convergence results for the EM approach to mixtures of experts architectures.
Neural Networks 8(9): 1409-1431 (1995) |
| 1994 |
|---|
| 25 | | Michael I. Jordan:
A Statistical Approach to Decision Tree Modeling.
COLT 1994: 13-20 |
| 24 | | Satinder P. Singh,
Tommi Jaakkola,
Michael I. Jordan:
Learning Without State-Estimation in Partially Observable Markovian Decision Processes.
ICML 1994: 284-292 |
| 23 | | Michael I. Jordan:
A Statistical Approach to Decision Tree Modeling.
ICML 1994: 363-370 |
| 22 | | Zoubin Ghahramani,
Daniel M. Wolpert,
Michael I. Jordan:
Computational Structure of coordinate transformations: A generalization study.
NIPS 1994: 1125-1132 |
| 21 | | Tommi Jaakkola,
Satinder P. Singh,
Michael I. Jordan:
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems.
NIPS 1994: 345-352 |
| 20 | | Satinder P. Singh,
Tommi Jaakkola,
Michael I. Jordan:
Reinforcement Learning with Soft State Aggregation.
NIPS 1994: 361-368 |
| 19 | | Daniel M. Wolpert,
Zoubin Ghahramani,
Michael I. Jordan:
Forward dynamic models in human motor control: Psychophysical evidence.
NIPS 1994: 43-50 |
| 18 | | Lawrence K. Saul,
Michael I. Jordan:
Boltzmann Chains and Hidden Markov Models.
NIPS 1994: 435-442 |
| 17 | | Lei Xu,
Michael I. Jordan,
Geoffrey E. Hinton:
An Alternative Model for Mixtures of Experts.
NIPS 1994: 633-640 |
| 16 | | David A. Cohn,
Zoubin Ghahramani,
Michael I. Jordan:
Active Learning with Statistical Models.
NIPS 1994: 705-712 |
| 15 | | Michael I. Jordan,
Robert A. Jacobs:
Hierarchical Mixtures of Experts and the EM Algorithm.
Neural Computation 6(2): 181-214 (1994) |
| 14 | | Lawrence K. Saul,
Michael I. Jordan:
Learning in Boltzmann Trees.
Neural Computation 6(6): 1174-1184 (1994) |
| 13 | | Tommi Jaakkola,
Michael I. Jordan,
Satinder P. Singh:
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms.
Neural Computation 6(6): 1185-1201 (1994) |
| 1993 |
|---|
| 12 | | Michael I. Jordan,
Robert A. Jacobs:
Supervised Learning and Divide-and-Conquer: A Statistical Approach.
ICML 1993: 159-166 |
| 11 | | 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 |
| 10 | | Zoubin Ghahramani,
Michael I. Jordan:
Supervised learning from incomplete data via an EM approach.
NIPS 1993: 120-127 |
| 9 | | Tommi Jaakkola,
Michael I. Jordan,
Satinder P. Singh:
Convergence of Stochastic Iterative Dynamic Programming Algorithms.
NIPS 1993: 703-710 |
| 1992 |
|---|
| 8 | | Daphne Bavelier,
Michael I. Jordan:
A Dynamical Model of Priming and Repetition Blindness.
NIPS 1992: 879-886 |
| 7 | | Michael I. Jordan,
David E. Rumelhart:
Forward Models: Supervised Learning with a Distal Teacher.
Cognitive Science 16(3): 307-354 (1992) |
| 1991 |
|---|
| 6 | | Michael I. Jordan,
David E. Rumelhart:
Internal World Models and Supervised Learning.
ML 1991: 70-74 |
| 5 | | Makoto Hirayama,
Eric Vatikiotis-Bateson,
Mitsuo Kawato,
Michael I. Jordan:
Forward Dynamics Modeling of Speech Motor Control Using Physiological Data.
NIPS 1991: 191-198 |
| 4 | | Michael I. Jordan,
Robert A. Jacobs:
Hierarchies of Adaptive Experts.
NIPS 1991: 985-992 |
| 3 | | 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) |
| 1990 |
|---|
| 2 | | Robert A. Jacobs,
Michael I. Jordan:
A Competitive Modular Connectionist Architecture.
NIPS 1990: 767-773 |
| 1989 |
|---|
| 1 | | Michael I. Jordan,
Robert A. Jacobs:
Learning to Control an Unstable System with Forward Modeling.
NIPS 1989: 324-331 |
