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Michael I. Jordan Vis

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*2009
177EEZhihua Zhang, Guang Dai, Michael I. Jordan: A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) 2009: 632-647
176EEArchana 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
175EEPercy Liang, Michael I. Jordan, Dan Klein: Learning from measurements in exponential families. ICML 2009: 81
174EEDonghui Yan, Ling Huang, Michael I. Jordan: Fast approximate spectral clustering. KDD 2009: 907-916
173EEMichael I. Jordan: Combinatorial stochastic processes and nonparametric Bayesian modeling. SODA 2009: 139
172EEWei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Detecting large-scale system problems by mining console logs. SOSP 2009: 117-132
171EEJunming 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
170EEChris 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
169EEEmily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. ICML 2008: 312-319
168EEPercy Liang, Michael I. Jordan: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. ICML 2008: 584-591
167EEGuillaume Obozinski, Martin J. Wainwright, Michael I. Jordan: High-dimensional support union recovery in multivariate regression. NIPS 2008: 1217-1224
166EEErik B. Sudderth, Michael I. Jordan: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592
165EEAlexandre Bouchard-Côté, Michael I. Jordan, Dan Klein: Efficient Inference in Phylogenetic InDel Trees. NIPS 2008: 177-184
164EEZhihua Zhang, Michael I. Jordan, Dit-Yan Yeung: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS 2008: 1969-1976
163EEEmily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464
162EELing Huang, Donghui Yan, Michael I. Jordan, Nina Taft: Spectral Clustering with Perturbed Data. NIPS 2008: 705-712
161EESimon Lacoste-Julien, Fei Sha, Michael I. Jordan: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904
160EESriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan: On the Inference of Ancestries in Admixed Populations. RECOMB 2008: 424-433
159EEWei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Mining Console Logs for Large-Scale System Problem Detection. SysML 2008
158EECharles A. Sutton, Michael I. Jordan: Probabilistic Inference in Queueing Networks. SysML 2008
157EEKurt 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
156EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by convex risk minimization CoRR abs/0809.0853: (2008)
155EEMartin J. Wainwright, Michael I. Jordan: Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning 1(1-2): 1-305 (2008)
154EEXuanLong 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
153EEMichael I. Jordan: Statistical Machine Learning and Computational Biology. BIBM 2007: 4
152EEJyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8
151EETao Li, Chris H. Q. Ding, Michael I. Jordan: Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. ICDM 2007: 577-582
150EEJyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124
149EEPercy Liang, Michael I. Jordan, Benjamin Taskar: A permutation-augmented sampler for DP mixture models. ICML 2007: 545-552
148EEJens Nilsson, Fei Sha, Michael I. Jordan: Regression on manifolds using kernel dimension reduction. ICML 2007: 697-704
147EELing 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
146EEPercy Liang, Dan Klein, Michael I. Jordan: Agreement-Based Learning. NIPS 2007
145EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. NIPS 2007
144EEBen Blum, Michael I. Jordan, David Kim, Rhiju Das, Philip Bradley, David Baker: Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. NIPS 2007
143EEEric P. Xing, Michael I. Jordan, Roded Sharan: Bayesian Haplotype Inference via the Dirichlet Process. Journal of Computational Biology 14(3): 267-284 (2007)
2006
142EESimon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan: Word Alignment via Quadratic Assignment. HLT-NAACL 2006
141EEEric 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
140EEAlice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken: Statistical debugging: simultaneous identification of multiple bugs. ICML 2006: 1105-1112
139EEBarbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner: A graphical model for predicting protein molecular function. ICML 2006: 297-304
138EELing Huang, XuanLong Nguyen, Minos N. Garofalakis, Michael I. Jordan, Anthony D. Joseph, Nina Taft: In-Network PCA and Anomaly Detection. NIPS 2006: 617-624
137EEZhihua Zhang, Michael I. Jordan: Bayesian Multicategory Support Vector Machines. UAI 2006
136EEDavid 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)
135EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On optimal quantization rules for some sequential decision problems CoRR abs/math/0608556: (2006)
134EEMartin 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)
133EEBenjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction, Dual Extragradient and Bregman Projections. Journal of Machine Learning Research 7: 1627-1653 (2006)
132EEFrancis R. Bach, Michael I. Jordan: Learning Spectral Clustering, With Application To Speech Separation. Journal of Machine Learning Research 7: 1963-2001 (2006)
131EEJon 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
130EEPeter 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
129EEFrancis R. Bach, Michael I. Jordan: Predictive low-rank decomposition for kernel methods. ICML 2005: 33-40
128EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Divergences, surrogate loss functions and experimental design. NIPS 2005
127EEPatrick Flaherty, Michael I. Jordan, Adam P. Arkin: Robust design of biological experiments. NIPS 2005
126EEBenjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction via the Extragradient Method. NIPS 2005
125EEBen Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan: Scalable statistical bug isolation. PLDI 2005: 15-26
124EEMichal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille: The DLR Hierarchy of Approximate Inference. UAI 2005: 493-500
123EEPatrick Flaherty, Guri Giaever, Jochen Kumm, Michael I. Jordan, Adam P. Arkin: A latent variable model for chemogenomic profiling. Bioinformatics 21(15): 3286-3293 (2005)
122EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On divergences, surrogate loss functions, and decentralized detection CoRR abs/math/0510521: (2005)
121EEXuanLong Nguyen, Michael I. Jordan, Bruno Sinopoli: A kernel-based learning approach to ad hoc sensor network localization. TOSN 1(1): 134-152 (2005)
2004
120EENeil D. Lawrence, John C. Platt, Michael I. Jordan: Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning 2004: 56-87
119EEMike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer: Failure Diagnosis Using Decision Trees. ICAC 2004: 36-43
118EEEric P. Xing, Roded Sharan, Michael I. Jordan: Bayesian haplo-type inference via the dirichlet process. ICML 2004
117EEXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Decentralized detection and classification using kernel methods. ICML 2004
116EEFrancis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm. ICML 2004
115EEDavid M. Blei, Michael I. Jordan: Variational methods for the Dirichlet process. ICML 2004
114EEAlexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet: A Direct Formulation for Sparse PCA Using Semidefinite Programming. NIPS 2004
113EEFrancis R. Bach, Michael I. Jordan: Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004
112EEFrancis R. Bach, Romain Thibaux, Michael I. Jordan: Computing regularization paths for learning multiple kernels. NIPS 2004
111EENeil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004
110EEYee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei: Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS 2004
109EEGert 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
108EEEric P. Xing, Michael I. Jordan: Graph Partition Strategies for Generalized Mean Field Inference. UAI 2004: 602-610
107EEJon D. McAuliffe, Lior Pachter, Michael I. Jordan: Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinformatics 20(12): 1850-1860 (2004)
106EEGert 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)
105EEAlexandre 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)
104EEEric 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)
103EEChiranjib 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)
102EEGert 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)
101EEKenji 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
100EEEric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp: LOGOS: a modular Bayesian model for de novo motif detection. CSB 2003: 266-276
99EEFernando De Bernardinis, Michael I. Jordan, Alberto L. Sangiovanni-Vincentelli: Support vector machines for analog circuit performance representation. DAC 2003: 964-969
98EEAndrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry: Autonomous Helicopter Flight via Reinforcement Learning. NIPS 2003
97EEDavid M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum: Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003
96EEKenji Fukumizu, Francis R. Bach, Michael I. Jordan: Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003
95EEPeter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003
94EEFrancis R. Bach, Michael I. Jordan: Learning Spectral Clustering. NIPS 2003
93EEXuanLong Nguyen, Michael I. Jordan: On the Concentration of Expectation and Approximate Inference in Layered Networks. NIPS 2003
92EEMartin J. Wainwright, Michael I. Jordan: Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. NIPS 2003
91EEAlice X. Zheng, Michael I. Jordan, Ben Liblit, Alexander Aiken: Statistical Debugging of Sampled Programs. NIPS 2003
90EEBen Liblit, Alexander Aiken, Alice X. Zheng, Michael I. Jordan: Bug isolation via remote program sampling. PLDI 2003: 141-154
89EEDavid 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
87EEKobus 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)
86EEDavid M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993-1022 (2003)
85EEFrancis 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)
83EEChiranjib 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
81EEFrancis R. Bach, Michael I. Jordan: Learning Graphical Models with Mercer Kernels. NIPS 2002: 1009-1016
80EEEric 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
79EEEmanuel Todorov, Michael I. Jordan: A Minimal Intervention Principle for Coordinated Movement. NIPS 2002: 27-34
78EEEric 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
77EEGert 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
74EEL. R. Grate, Chiranjib Bhattacharyya, Michael I. Jordan, I. Saira Mian: Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. WABI 2002: 1-9
73EEFrancis R. Bach, Michael I. Jordan: Kernel Independent Component Analysis. Journal of Machine Learning Research 3: 1-48 (2002)
72EEGert 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)
71EEMichael 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
67EEFrancis R. Bach, Michael I. Jordan: Thin Junction Trees. NIPS 2001: 569-576
66EEDavid M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. NIPS 2001: 601-608
65EEGert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan: Minimax Probability Machine. NIPS 2001: 801-807
64EEAndrew Y. Ng, Michael I. Jordan: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001: 841-848
63EEAndrew 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
61EEAmol 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
59EEAndrew Y. Ng, Michael I. Jordan: PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000: 406-415
58EEMarina 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
56EEAndrew Y. Ng, Michael I. Jordan: Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999: 533-539
55EEKevin P. Murphy, Yair Weiss, Michael I. Jordan: Loopy Belief Propagation for Approximate Inference: An Empirical Study. UAI 1999: 467-475
54EETommi 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
50EEThomas Hofmann, Jan Puzicha, Michael I. Jordan: Learning from Dyadic Data. NIPS 1998: 466-472
49EENeil 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)
42EEPadhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997)
1996
41EELawrence K. Saul, Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996: 267-273
40EETommi Jaakkola, Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493
39EEMichael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul: Hidden Markov Decision Trees. NIPS 1996: 501-507
38EEMarina Meila, Michael I. Jordan: Triangulation by Continuous Embedding. NIPS 1996: 557-563
37EETommi 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)
35EELawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks CoRR cs.AI/9603102: (1996)
34EEDavid 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
31EEMarina Meila, Michael I. Jordan: Learning Fine Motion by Markov Mixtures of Experts. NIPS 1995: 1003-1009
30EEPhilip N. Sabes, Michael I. Jordan: Reinforcement Learning by Probability Matching. NIPS 1995: 1080-1086
29EEZoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. NIPS 1995: 472-478
28EELawrence K. Saul, Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492
27EETommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534
26EEMichael I. Jordan, Lei Xu: Convergence results for the EM approach to mixtures of experts architectures. Neural Networks 8(9): 1409-1431 (1995)
1994
25EEMichael 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
22EEZoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan: Computational Structure of coordinate transformations: A generalization study. NIPS 1994: 1125-1132
21EETommi Jaakkola, Satinder P. Singh, Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352
20EESatinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368
19EEDaniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan: Forward dynamic models in human motor control: Psychophysical evidence. NIPS 1994: 43-50
18EELawrence K. Saul, Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442
17EELei Xu, Michael I. Jordan, Geoffrey E. Hinton: An Alternative Model for Mixtures of Experts. NIPS 1994: 633-640
16EEDavid A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. NIPS 1994: 705-712
15EEMichael I. Jordan, Robert A. Jacobs: Hierarchical Mixtures of Experts and the EM Algorithm. Neural Computation 6(2): 181-214 (1994)
14EELawrence K. Saul, Michael I. Jordan: Learning in Boltzmann Trees. Neural Computation 6(6): 1174-1184 (1994)
13EETommi 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
10EEZoubin Ghahramani, Michael I. Jordan: Supervised learning from incomplete data via an EM approach. NIPS 1993: 120-127
9EETommi Jaakkola, Michael I. Jordan, Satinder P. Singh: Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710
1992
8EEDaphne 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
5EEMakoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan: Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. NIPS 1991: 191-198
4EEMichael 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
2EERobert A. Jacobs, Michael I. Jordan: A Competitive Modular Connectionist Architecture. NIPS 1990: 767-773
1989
1EEMichael I. Jordan, Robert A. Jacobs: Learning to Control an Unstable System with Forward Modeling. NIPS 1989: 324-331

Coauthor Index

1Alexander Aiken (Alex Aiken) [90] [91] [125] [140]
2Christophe Andrieu [84]
3Adam P. Arkin [123] [127]
4Francis R. Bach (Francis Bach) [67] [73] [76] [81] [85] [94] [96] [101] [112] [113] [116] [129] [132]
5David Baker [144]
6Kobus Barnard [87]
7Peter L. Bartlett [82] [95] [102]
8Andrew G. Barto [3] [11]
9Daphne Bavelier [8]
10Matthew J. Beal [110]
11Fernando De Bernardinis [99]
12Chiranjib Bhattacharyya (Chiru Bhattacharyya) [65] [72] [74] [83] [103]
13Tijl De Bie [106]
14Lukas Biewald [130]
15Christopher M. Bishop [36] [44] [46] [49]
16Mina J. Bissell [83]
17David M. Blei [66] [86] [87] [89] [97] [110] [115] [131] [136]
18Ben Blum [144]
19Peter Bodík [130]
20Alexandre Bouchard-Côté [165]
21Philip Bradley [144]
22Steven E. Brenner [139]
23Eric A. Brewer [119]
24George Candea [130]
25Mike Y. Chen [119]
26David A. Cohn [16] [33] [34]
27Nello Cristianini [82] [102] [106] [109]
28Guang Dai [177]
29Rhiju Das [144]
30Umeshwar Dayal [176]
31Minghua Deng [109]
32Amol Deshpande [61]
33Chris H. Q. Ding (Hong Q. Ding) [151] [170]
34Arnaud Doucet [84]
35Pinar Duygulu [87]
36Barbara E. Engelhardt [139]
37Patrick Flaherty [123] [127]
38David A. Forsyth [87]
39Armando Fox [130] [159] [172] [176]
40Emily B. Fox [163] [169]
41K. Franks [136]
42Nando de Freitas [84] [87]
43Greg Friedman [130]
44Kenji Fukumizu [96] [101]
45Archana Ganapathi [176]
46Minos N. Garofalakis [61] [138] [147]
47Zoubin Ghahramani [10] [16] [19] [22] [29] [33] [34] [39] [43] [52]
48Laurent El Ghaoui [65] [72] [77] [82] [102] [103] [105] [114]
49Guri Giaever [123]
50L. R. Grate [74] [83] [103]
51Thomas L. Griffiths [97] [157]
52Eran Halperin [160]
53David Heckerman [42]
54Joseph M. Hellerstein [147]
55Geoffrey E. Hinton [17]
56Makoto Hirayama [5]
57Thomas Hofmann [50]
58John F. Houde [47]
59Ling Huang [138] [147] [159] [162] [172] [174]
60Jonathan Hui [130]
61Tommi Jaakkola [9] [13] [20] [21] [24] [27] [32] [35] [37] [40] [46] [52] [54]
62Robert A. Jacobs [1] [2] [3] [4] [11] [12] [15]
63Anthony D. Joseph [138] [147]
64Richard M. Karp [69] [80] [100] [104]
65Mitsuo Kawato [5]
66Michael J. Kearns [51]
67David Kim [144]
68H. Jin Kim [98]
69Gad Kimmel [160]
70Jyri J. Kivinen [150] [152]
71Dan Klein [142] [146] [165] [175]
72Jochen Kumm [123]
73Harumi A. Kuno [176]
74Simon Lacoste-Julien [126] [133] [142] [161]
75Gert R. G. Lanckriet [65] [72] [77] [82] [102] [105] [106] [109] [114] [116]
76Neil D. Lawrence [46] [49] [111] [120]
77Helen Levine [130]
78Tao Li [151] [170]
79Percy Liang [146] [149] [168] [175]
80Ben Liblit [90] [91] [125] [140]
81Jim Lloyd [119]
82Jinwen Ma [60]
83Jon D. McAuliffe [95] [107] [131]
84Marina Meila [31] [38] [45] [58]
85I. Saira Mian [74] [83] [103] [136]
86Kurt T. Miller [157]
87F. J. Molina [83]
88Michael C. Mozer (Michael Mozer) [48]
89Kevin P. Murphy [55]
90Mayur Naik [125] [140]
91Andrew Y. Ng [56] [59] [62] [63] [64] [66] [68] [70] [78] [86] [98]
92XuanLong Nguyen [93] [117] [121] [122] [128] [135] [138] [145] [147] [154] [156]
93Jens Nilsson [148]
94William Stafford Noble [106] [109]
95Guillaume Obozinski [167]
96Lior Pachter [107]
97Kayur Patel [130]
98David A. Patterson [130] [159] [172] [176]
99Thomas Petsche [48]
100John C. Platt [120]
101Jan Puzicha [50]
102D. Radisky [83]
103A. Rizki [83]
104Michal Rosen-Zvi [124]
105David E. Rumelhart [6] [7]
106Stuart J. Russell [78] [80] [88]
107Philip N. Sabes [30]
108Alberto L. Sangiovanni-Vincentelli [99]
109Sriram Sankararaman [160]
110Shankar Sastry (Shankar S. Sastry) [98]
111Lawrence K. Saul [14] [18] [27] [28] [32] [35] [39] [41] [52] [53] [57]
112Terrence J. Sejnowski [71]
113Fei Sha [148] [161]
114Roded Sharan [118] [143]
115Satinder P. Singh [9] [13] [20] [21] [24]
116Bruno Sinopoli [121]
117Padhraic Smyth [42]
118Kyung-Ah Sohn [141]
119Sara A. Solla [51]
120Yun S. Song [171]
121Erik B. Sudderth [150] [152] [163] [166] [169]
122Charles A. Sutton [158]
123Nina Taft (Nina Taft Plotkin) [138] [147] [162]
124Benjamin Taskar (Ben Taskar) [126] [133] [142] [149]
125Sekhar Tatikonda [75]
126Yee Whye Teh [110] [141]
127Joshua B. Tenenbaum [97]
128Romain Thibaux [112]
129Emanuel Todorov [79]
130Gilman Tolle [130]
131Eric Vatikiotis-Bateson [5]
132Martin J. Wainwright [92] [117] [122] [128] [134] [135] [145] [154] [155] [156] [167]
133Yair Weiss [55] [63]
134Janet L. Wiener [176]
135Alan S. Willsky [163] [169]
136Daniel M. Wolpert [19] [22]
137Wei Wu [100] [104]
138Eric P. Xing [69] [78] [80] [88] [100] [104] [108] [118] [141] [143]
139Lei Xu [17] [26] [60]
140Wei Xu [159] [172]
141Donghui Yan [162] [174]
142Dit-Yan Yeung [164]
143Junming Yin [171]
144Alan L. Yuille [124]
145Zhihua Zhang [137] [164] [177]
146Alice X. Zheng [62] [68] [90] [91] [119] [125] [140]
147Alexandre d'Aspremont [105] [114]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)