Michael I. Jordan

University of California, Berkeley, Department of Electrical Engineering and Computer Science

	2013
c168		Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan: MLbase: A Distributed Machine-learning System. CIDR 2013
i34		Francis R. Bach, Michael I. Jordan: Tree-dependent Component Analysis. CoRR abs/1301.0554 (2013)
i33		Sekhar Tatikonda, Michael I. Jordan: Loopy Belief Propogation and Gibbs Measures. CoRR abs/1301.0605 (2013)
i32		Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart J. Russell: Variational MCMC. CoRR abs/1301.2266 (2013)
i31		Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan: Efficient Stepwise Selection in Decomposable Models. CoRR abs/1301.2267 (2013)
i30		Andrew Y. Ng, Michael I. Jordan: PEGASUS: A Policy Search Method for Large MDPs and POMDPs. CoRR abs/1301.3878 (2013)
i29		Kevin P. Murphy, Yair Weiss, Michael I. Jordan: Loopy Belief Propagation for Approximate Inference: An Empirical Study. CoRR abs/1301.6725 (2013)
i28		Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. CoRR abs/1301.7393 (2013)
i27		John C. Duchi, Michael I. Jordan, Martin J. Wainwright: Local Privacy and Statistical Minimax Rates. CoRR abs/1302.3203 (2013)
i26		Tommi Jaakkola, Michael I. Jordan: Computing Upper and Lower Bounds on Likelihoods in Intractable Networks. CoRR abs/1302.3586 (2013)
i25		Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan: Divide-and-Conquer Subspace Segmentation. CoRR abs/1304.5583 (2013)
	2012
j69		John William Paisley, David M. Blei, Michael I. Jordan: Stick-Breaking Beta Processes and the Poisson Process. Journal of Machine Learning Research - Proceedings Track 22: 850-858 (2012)
j68		Paul Lukowicz, Sanjiv Nanda, Vidya Narayanan, Hal Albelson, Deborah L. McGuinness, Michael I. Jordan: Qualcomm Context-Awareness Symposium Sets Research Agenda for Context-Aware Smartphones. IEEE Pervasive Computing 11(1): 76-79 (2012)
j67		John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan: Ergodic Mirror Descent. SIAM Journal on Optimization 22(4): 1549-1578 (2012)
c167		Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan: The Big Data Bootstrap. ICML 2012
c166		Brian Kulis, Michael I. Jordan: Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML 2012
c165		John William Paisley, David M. Blei, Michael I. Jordan: Variational Bayesian Inference with Stochastic Search. ICML 2012
c164		Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan: Nonparametric Link Prediction in Dynamic Networks. ICML 2012
c163		Michael I. Jordan: Divide-and-conquer and statistical inference for big data. KDD 2012: 4
c162		Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan: Active spectral clustering via iterative uncertainty reduction. KDD 2012: 1339-1347
c161		John C. Duchi, Michael I. Jordan, Martin J. Wainwright: Privacy Aware Learning. NIPS 2012: 1439-1447
c160		John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. NIPS 2012: 1448-1456
c159		Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön: Ancestor Sampling for Particle Gibbs. NIPS 2012: 2600-2608
c158		Ke Jiang, Brian Kulis, Michael I. Jordan: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS 2012: 3167-3175
i24		Aleksandr Simma, Michael I. Jordan: Modeling Events with Cascades of Poisson Processes. CoRR abs/1203.3516 (2012)
i23		John C. Duchi, Lester W. Mackey, Michael I. Jordan: The Asymptotics of Ranking Algorithms. CoRR abs/1204.1688 (2012)
i22		Alexandre Bouchard-Côté, Michael I. Jordan: Optimization of Structured Mean Field Objectives. CoRR abs/1205.2658 (2012)
i21		Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan: The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. CoRR abs/1206.3279 (2012)
i20		Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille: The DLR Hierarchy of Approximate Inference. CoRR abs/1207.1417 (2012)
i19		Eric P. Xing, Michael I. Jordan, Stuart J. Russell: Graph partition strategies for generalized mean field inference. CoRR abs/1207.4156 (2012)
i18		Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael I. Jordan, Samuel Madden: Active Learning for Crowd-Sourced Databases. CoRR abs/1209.3686 (2012)
i17		John C. Duchi, Michael I. Jordan, Martin J. Wainwright: Privacy Aware Learning. CoRR abs/1210.2085 (2012)
i16		John William Paisley, Chong Wang, David M. Blei, Michael I. Jordan: Nested Hierarchical Dirichlet Processes. CoRR abs/1210.6738 (2012)
i15		Venkat Chandrasekaran, Michael I. Jordan: Computational and Statistical Tradeoffs via Convex Relaxation. CoRR abs/1211.1073 (2012)
i14		Eric P. Xing, Michael I. Jordan, Stuart J. Russell: A Generalized Mean Field Algorithm for Variational Inference in Exponential Families. CoRR abs/1212.2512 (2012)
	2011
j66		Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic: Nonparametric Combinatorial Sequence Models. Journal of Computational Biology 18(11): 1649-1660 (2011)
j65		Zhihua Zhang, Guang Dai, Michael I. Jordan: Bayesian Generalized Kernel Mixed Models. Journal of Machine Learning Research 12: 111-139 (2011)
j64		Donglin Niu, Jennifer G. Dy, Michael I. Jordan: Dimensionality Reduction for Spectral Clustering. Journal of Machine Learning Research - Proceedings Track 15: 552-560 (2011)
j63		Lawrence Carin, Richard G. Baraniuk, Volkan Cevher, David B. Dunson, Michael I. Jordan, Guillermo Sapiro, Michael B. Wakin: Learning Low-Dimensional Signal Models. IEEE Signal Process. Mag. 28(2): 39-51 (2011)
j62		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Transactions on Signal Processing 59(4): 1569-1585 (2011)
c157		Percy Liang, Michael I. Jordan, Dan Klein: Learning Dependency-Based Compositional Semantics. ACL 2011: 590-599
c156		Min-Yu Huang, Lester W. Mackey, Soile V. E. Keränen, Gunther H. Weber, Michael I. Jordan, David W. Knowles, Mark D. Biggin, Bernd Hamann: Visually Relating Gene Expression and in vivo DNA Binding Data. BIBM 2011: 586-589
c155		Alex Shyr, Trevor Darrell, Michael I. Jordan, Raquel Urtasun: Supervised hierarchical Pitman-Yor process for natural scene segmentation. CVPR 2011: 2281-2288
c154		Beth Trushkowsky, Peter Bodík, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson: The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements. FAST 2011: 163-176
c153		Yue Guan, Jennifer G. Dy, Michael I. Jordan: A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. ICML 2011: 1073-1080
c152		Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan: Divide-and-Conquer Matrix Factorization. NIPS 2011: 1134-1142
c151		Fabian L. Wauthier, Michael I. Jordan: Bayesian Bias Mitigation for Crowdsourcing. NIPS 2011: 1800-1808
c150		Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic: Nonparametric Combinatorial Sequence Models. RECOMB 2011: 516-530
c149		Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica: Managing data transfers in computer clusters with orchestra. SIGCOMM 2011: 98-109
i13		Donghui Yan, Aiyou Chen, Michael I. Jordan: Cluster Forests. CoRR abs/1104.2930 (2011)
i12		Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. CoRR abs/1105.5462 (2011)
i11		Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan: Divide-and-Conquer Matrix Factorization. CoRR abs/1107.0789 (2011)
i10		Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan: Non-parametric Link Prediction. CoRR abs/1109.1077 (2011)
i9		Percy Liang, Michael I. Jordan, Dan Klein: Learning Dependency-Based Compositional Semantics. CoRR abs/1109.6841 (2011)
i8		Brian Kulis, Michael I. Jordan: Revisiting k-means: New Algorithms via Bayesian Nonparametrics. CoRR abs/1111.0352 (2011)
	2010
j61		Sriram Sankararaman, Fei Sha, Jack F. Kirsch, Michael I. Jordan, Kimmen Sjölander: Active site prediction using evolutionary and structural information. Bioinformatics 26(5): 617-624 (2010)
j60		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)
j59		Charles A. Sutton, Michael I. Jordan: Inference and Learning in Networks of Queues. Journal of Machine Learning Research - Proceedings Track 9: 796-803 (2010)
j58		Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan: Bayesian Generalized Kernel Models. Journal of Machine Learning Research - Proceedings Track 9: 972-979 (2010)
j57		Zhihua Zhang, Guang Dai, Michael I. Jordan: Matrix-Variate Dirichlet Process Mixture Models. Journal of Machine Learning Research - Proceedings Track 9: 980-987 (2010)
j56		Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan: Regularized Discriminant Analysis, Ridge Regression and Beyond. Journal of Machine Learning Research 11: 2199-2228 (2010)
j55		Chris H. Q. Ding, Tao Li, Michael I. Jordan: Convex and Semi-Nonnegative Matrix Factorizations. IEEE Trans. Pattern Anal. Mach. Intell. 32(1): 45-55 (2010)
j54		Daniel Ting, Guoli Wang, Maxim V. Shapovalov, Rajib Mitra, Michael I. Jordan, Roland L. Dunbrack Jr.: Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model. PLoS Computational Biology 6(4) (2010)
j53		Guillaume Obozinski, Ben Taskar, Michael I. Jordan: Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing 20(2): 231-252 (2010)
j52		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization. IEEE Transactions on Information Theory 56(11): 5847-5861 (2010)
c148		Peter Bodík, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson: Characterizing, modeling, and generating workload spikes for stateful services. SoCC 2010: 241-252
c147		Alex Shyr, Raquel Urtasun, Michael I. Jordan: Sufficient dimension reduction for visual sequence classification. CVPR 2010: 3610-3617
c146		Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Detecting Large-Scale System Problems by Mining Console Logs. ICML 2010: 37-46
c145		John C. Duchi, Lester W. Mackey, Michael I. Jordan: On the Consistency of Ranking Algorithms. ICML 2010: 327-334
c144		Percy Liang, Michael I. Jordan, Dan Klein: Learning Programs: A Hierarchical Bayesian Approach. ICML 2010: 639-646
c143		Lester W. Mackey, David Weiss, Michael I. Jordan: Mixed Membership Matrix Factorization. ICML 2010: 711-718
c142		Donglin Niu, Jennifer G. Dy, Michael I. Jordan: Multiple Non-Redundant Spectral Clustering Views. ICML 2010: 831-838
c141		Daniel Ting, Ling Huang, Michael I. Jordan: An Analysis of the Convergence of Graph Laplacians. ICML 2010: 1079-1086
c140		Percy Liang, Michael I. Jordan, Dan Klein: Type-Based MCMC. HLT-NAACL 2010: 573-581
c139		Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan: Tree-Structured Stick Breaking for Hierarchical Data. NIPS 2010: 19-27
c138		Alexandre Bouchard-Côté, Michael I. Jordan: Variational Inference over Combinatorial Spaces. NIPS 2010: 280-288
c137		Ariel Kleiner, Ali Rahimi, Michael I. Jordan: Random Conic Pursuit for Semidefinite Programming. NIPS 2010: 1135-1143
c136		Meihong Wang, Fei Sha, Michael I. Jordan: Unsupervised Kernel Dimension Reduction. NIPS 2010: 2379-2387
c135		Fabian L. Wauthier, Michael I. Jordan: Heavy-Tailed Process Priors for Selective Shrinkage. NIPS 2010: 2406-2414
c134		Aleksandr Simma, Michael I. Jordan: Modeling Events with Cascades of Poisson Processes. UAI 2010: 546-555
i7		Charles A. Sutton, Michael I. Jordan: Bayesian Inference in Queueing Networks. CoRR abs/1001.3355 (2010)
	2009
j51		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)
j50		Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung: Coherence Functions for Multicategory Margin-based Classification Methods. Journal of Machine Learning Research - Proceedings Track 5: 647-654 (2009)
j49		Zhihua Zhang, Michael I. Jordan: Latent Variable Models for Dimensionality Reduction. Journal of Machine Learning Research - Proceedings Track 5: 655-662 (2009)
c133		Percy Liang, Michael I. Jordan, Dan Klein: Learning Semantic Correspondences with Less Supervision. ACL/IJCNLP 2009: 91-99
c132		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
c131		Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Online System Problem Detection by Mining Patterns of Console Logs. ICDM 2009: 588-597
c130		Percy Liang, Michael I. Jordan, Dan Klein: Learning from measurements in exponential families. ICML 2009: 81
c129		Donghui Yan, Ling Huang, Michael I. Jordan: Fast approximate spectral clustering. KDD 2009: 907-916
c128		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Sharing Features among Dynamical Systems with Beta Processes. NIPS 2009: 549-557
c127		Percy Liang, Francis R. Bach, Guillaume Bouchard, Michael I. Jordan: Asymptotically Optimal Regularization in Smooth Parametric Models. NIPS 2009: 1132-1140
c126		Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan: Nonparametric Latent Feature Models for Link Prediction. NIPS 2009: 1276-1284
c125		Zhihua Zhang, Guang Dai, Michael I. Jordan: A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) 2009: 632-647
c124		Michael I. Jordan: Combinatorial stochastic processes and nonparametric Bayesian modeling. SODA 2009: 139
c123		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
c122		Alexandre Bouchard-Côté, Michael I. Jordan: Optimization of Structured Mean Field Objectives. UAI 2009: 67-74
	2008
j48		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)
j47		Patrick Flaherty, Mala L. Radhakrishnan, Tuan Dinh, Robert A. Rebres, Tamara I. Roach, Michael I. Jordan, Adam P. Arkin: A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction. PLoS Computational Biology 4(9) (2008)
j46		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)
c121		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
c120		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. ICML 2008: 312-319
c119		Percy Liang, Michael I. Jordan: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. ICML 2008: 584-591
c118		Alexandre Bouchard-Côté, Michael I. Jordan, Dan Klein: Efficient Inference in Phylogenetic InDel Trees. NIPS 2008: 177-184
c117		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464
c116		Ling Huang, Donghui Yan, Michael I. Jordan, Nina Taft: Spectral Clustering with Perturbed Data. NIPS 2008: 705-712
c115		Simon Lacoste-Julien, Fei Sha, Michael I. Jordan: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904
c114		Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan: High-dimensional support union recovery in multivariate regression. NIPS 2008: 1217-1224
c113		Erik B. Sudderth, Michael I. Jordan: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592
c112		Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS 2008: 1969-1976
c111		Charles A. Sutton, Michael I. Jordan: Probabilistic Inference in Queueing Networks. SysML 2008
c110		Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Mining Console Logs for Large-Scale System Problem Detection. SysML 2008
c109		Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan: On the Inference of Ancestries in Admixed Populations. RECOMB 2008: 424-433
c108		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
i6		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by convex risk minimization. CoRR abs/0809.0853 (2008)
	2007
j45		Eric P. Xing, Michael I. Jordan, Roded Sharan: Bayesian Haplotype Inference via the Dirichlet Process. Journal of Computational Biology 14(3): 267-284 (2007)
j44		Romain Thibaux, Michael I. Jordan: Hierarchical Beta Processes and the Indian Buffet Process. Journal of Machine Learning Research - Proceedings Track 2: 564-571 (2007)
c107		Michael I. Jordan: Statistical Machine Learning and Computational Biology. BIBM 2007: 4
c106		Percy Liang, Slav Petrov, Michael I. Jordan, Dan Klein: The Infinite PCFG Using Hierarchical Dirichlet Processes. EMNLP-CoNLL 2007: 688-697
c105		Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8
c104		Tao Li, Chris H. Q. Ding, Michael I. Jordan: Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. ICDM 2007: 577-582
c103		Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124
c102		Percy Liang, Michael I. Jordan, Benjamin Taskar: A permutation-augmented sampler for DP mixture models. ICML 2007: 545-552
c101		Jens Nilsson, Fei Sha, Michael I. Jordan: Regression on manifolds using kernel dimension reduction. ICML 2007: 697-704
c100		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
c99		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
c98		Percy Liang, Dan Klein, Michael I. Jordan: Agreement-Based Learning. NIPS 2007
c97		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. NIPS 2007
	2006
j43		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)
j42		Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction, Dual Extragradient and Bregman Projections. Journal of Machine Learning Research 7: 1627-1653 (2006)
j41		Francis R. Bach, Michael I. Jordan: Learning Spectral Clustering, With Application To Speech Separation. Journal of Machine Learning Research 7: 1963-2001 (2006)
j40		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)
j39		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)
c96		Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner: A graphical model for predicting protein molecular function. ICML 2006: 297-304
c95		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
c94		Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken: Statistical debugging: simultaneous identification of multiple bugs. ICML 2006: 1105-1112
c93		Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan: Word Alignment via Quadratic Assignment. HLT-NAACL 2006
c92		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
c91		Zhihua Zhang, Michael I. Jordan: Bayesian Multicategory Support Vector Machines. UAI 2006
i5		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On optimal quantization rules for some sequential decision problems. CoRR abs/math/0608556 (2006)
	2005
j38		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)
j37		Barbara E. Engelhardt, Michael I. Jordan, Kathryn E. Muratore, Steven E. Brenner: Protein Molecular Function Prediction by Bayesian Phylogenomics. PLoS Computational Biology 1(5) (2005)
j36		XuanLong Nguyen, Michael I. Jordan, Bruno Sinopoli: A kernel-based learning approach to ad hoc sensor network localization. TOSN 1(1): 134-152 (2005)
c90		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
c89		Francis R. Bach, Michael I. Jordan: Predictive low-rank decomposition for kernel methods. ICML 2005: 33-40
c88		Patrick Flaherty, Michael I. Jordan, Adam P. Arkin: Robust design of biological experiments. NIPS 2005
c87		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Divergences, surrogate loss functions and experimental design. NIPS 2005
c86		Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction via the Extragradient Method. NIPS 2005
c85		Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan: Scalable statistical bug isolation. PLDI 2005: 15-26
c84		Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille: The DLR Hierarchy of Approximate Inference. UAI 2005: 493-500
i4		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On divergences, surrogate loss functions, and decentralized detection. CoRR abs/math/0510521 (2005)
	2004
j35		Jon D. McAuliffe, Lior Pachter, Michael I. Jordan: Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinformatics 20(12): 1850-1860 (2004)
j34		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)
j33		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)
j32		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)
j31		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)
j30		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)
j29		Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Michael I. Jordan, Shankar S. Sastry: Kalman filtering with intermittent observations. IEEE Trans. Automat. Contr. 49(9): 1453-1464 (2004)
j28		Francis R. Bach, Michael I. Jordan: Learning graphical models for stationary time series. IEEE Transactions on Signal Processing 52(8): 2189-2199 (2004)
c83		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
c82		Mike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer: Failure Diagnosis Using Decision Trees. ICAC 2004: 36-43
c81		Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm. ICML 2004
c80		David M. Blei, Michael I. Jordan: Variational methods for the Dirichlet process. ICML 2004
c79		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Decentralized detection and classification using kernel methods. ICML 2004
c78		Eric P. Xing, Roded Sharan, Michael I. Jordan: Bayesian haplo-type inference via the dirichlet process. ICML 2004
c77		Francis R. Bach, Michael I. Jordan: Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004
c76		Francis R. Bach, Romain Thibaux, Michael I. Jordan: Computing regularization paths for learning multiple kernels. NIPS 2004
c75		Neil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004
c74		Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei: Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS 2004
c73		Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet: A Direct Formulation for Sparse PCA Using Semidefinite Programming. NIPS 2004
c72		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
c71		Eric P. Xing, Michael I. Jordan: Graph Partition Strategies for Generalized Mean Field Inference. UAI 2004: 602-610
i3		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)
	2003
j27		David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993-1022 (2003)
j26		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)
j25		Francis R. Bach, Michael I. Jordan: Beyond Independent Components: Trees and Clusters. Journal of Machine Learning Research 4: 1205-1233 (2003)
j24		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)
j23		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)
c70		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
c69		Fernando De Bernardinis, Michael I. Jordan, Alberto L. Sangiovanni-Vincentelli: Support vector machines for analog circuit performance representation. DAC 2003: 964-969
c68		Francis R. Bach, Michael I. Jordan: Learning Spectral Clustering. NIPS 2003
c67		Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003
c66		David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum: Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003
c65		Kenji Fukumizu, Francis R. Bach, Michael I. Jordan: Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003
c64		Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry: Autonomous Helicopter Flight via Reinforcement Learning. NIPS 2003
c63		XuanLong Nguyen, Michael I. Jordan: On the Concentration of Expectation and Approximate Inference in Layered Networks. NIPS 2003
c62		Martin J. Wainwright, Michael I. Jordan: Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. NIPS 2003
c61		Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alexander Aiken: Statistical Debugging of Sampled Programs. NIPS 2003
c60		Ben Liblit, Alexander Aiken, Alice X. Zheng, Michael I. Jordan: Bug isolation via remote program sampling. PLDI 2003: 141-154
c59		David M. Blei, Michael I. Jordan: Modeling annotated data. SIGIR 2003: 127-134
c58		Eric P. Xing, Michael I. Jordan, Stuart J. Russell: A generalized mean field algorithm for variational inference in exponential families. UAI 2003: 583-591
	2002
j22		Francis R. Bach, Michael I. Jordan: Kernel Independent Component Analysis. Journal of Machine Learning Research 3: 1-48 (2002)
j21		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)
j20		Michael I. Jordan, Terrence J. Sejnowski: Graphical Models: Foundations of Neural Computation. Pattern Anal. Appl. 5(4): 401-402 (2002)
c57		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
c56		Emanuel Todorov, Michael I. Jordan: A Minimal Intervention Principle for Coordinated Movement. NIPS 2002: 27-34
c55		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
c54		Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan: Robust Novelty Detection with Single-Class MPM. NIPS 2002: 905-912
c53		Francis R. Bach, Michael I. Jordan: Learning Graphical Models with Mercer Kernels. NIPS 2002: 1009-1016
c52		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
c51		Francis R. Bach, Michael I. Jordan: Tree-dependent Component Analysis. UAI 2002: 36-44
c50		Sekhar Tatikonda, Michael I. Jordan: Loopy Belief Propogation and Gibbs Measures. UAI 2002: 493-500
c49		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
	2001
j19		Jinwen Ma, Lei Xu, Michael I. Jordan: Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. Neural Computation 12(12): 2881-2907 (2001)
c48		Andrew Y. Ng, Michael I. Jordan: Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001: 377-384
c47		Eric P. Xing, Michael I. Jordan, Richard M. Karp: Feature selection for high-dimensional genomic microarray data. ICML 2001: 601-608
c46		Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan: Link Analysis, Eigenvectors and Stability. IJCAI 2001: 903-910
c45		Francis R. Bach, Michael I. Jordan: Thin Junction Trees. NIPS 2001: 569-576
c44		David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. NIPS 2001: 601-608
c43		Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan: Minimax Probability Machine. NIPS 2001: 801-807
c42		Andrew Y. Ng, Michael I. Jordan: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001: 841-848
c41		Andrew Y. Ng, Michael I. Jordan, Yair Weiss: On Spectral Clustering: Analysis and an algorithm. NIPS 2001: 849-856
c40		Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan: Stable Algorithms for Link Analysis. SIGIR 2001: 258-266
c39		Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan: Efficient Stepwise Selection in Decomposable Models. UAI 2001: 128-135
	2000
j18		Marina Meila, Michael I. Jordan: Learning with Mixtures of Trees. Journal of Machine Learning Research 1: 1-48 (2000)
j17		Lawrence K. Saul, Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12(6): 1313-1335 (2000)
c38		Andrew Y. Ng, Michael I. Jordan: PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000: 406-415
	1999
j16		Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999)
j15		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)
j14		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)
c37		Andrew Y. Ng, Michael I. Jordan: Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999: 533-539
c36		Kevin P. Murphy, Yair Weiss, Michael I. Jordan: Loopy Belief Propagation for Approximate Inference: An Empirical Study. UAI 1999: 467-475
	1998
c35		Thomas Hofmann, Jan Puzicha, Michael I. Jordan: Learning from Dyadic Data. NIPS 1998: 466-472
c34		Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327
e2		Michael I. Jordan, Michael J. Kearns, Sara A. Solla (Eds.): Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997]. The MIT Press 1998, isbn 0-262-10076-2
	1997
j13		Zoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. Machine Learning 29(2-3): 245-273 (1997)
j12		Padhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997)
p1		Michael I. Jordan, Christopher M. Bishop: Neural Networks. The Computer Science and Engineering Handbook 1997: 536-556
c33		Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997
c32		John F. Houde, Michael I. Jordan: Adaptation in Speech Motor Control. NIPS 1997
c31		Marina Meila, Michael I. Jordan: Estimating Dependency Structure as a Hidden Variable. NIPS 1997
e1		Michael Mozer, Michael I. Jordan, Thomas Petsche (Eds.): Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996. MIT Press 1997
	1996
j11		Michael I. Jordan, Christopher M. Bishop: Neural Networks. ACM Comput. Surv. 28(1): 73-75 (1996)
j10		Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996)
j9		David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. J. Artif. Intell. Res. (JAIR) 4: 129-145 (1996)
j8		Ethem Alpaydin, Michael I. Jordan: Local linear perceptrons for classification. IEEE Trans. Neural Netw. Learning Syst. 7(3): 788-794 (1996)
c30		Lawrence K. Saul, Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996: 267-273
c29		Tommi Jaakkola, Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493
c28		Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul: Hidden Markov Decision Trees. NIPS 1996: 501-507
c27		Marina Meila, Michael I. Jordan: Triangulation by Continuous Embedding. NIPS 1996: 557-563
c26		Tommi Jaakkola, Michael I. Jordan: Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996: 340-348
i2		Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. CoRR cs.AI/9603102 (1996)
i1		David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. CoRR cs.AI/9603104 (1996)
	1995
j7		Michael I. Jordan, Lei Xu: Convergence results for the EM approach to mixtures of experts architectures. Neural Networks 8(9): 1409-1431 (1995)
c25		Zoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. NIPS 1995: 472-478
c24		Lawrence K. Saul, Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492
c23		Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534
c22		Marina Meila, Michael I. Jordan: Learning Fine Motion by Markov Mixtures of Experts. NIPS 1995: 1003-1009
c21		Philip N. Sabes, Michael I. Jordan: Reinforcement Learning by Probability Matching. NIPS 1995: 1080-1086
	1994
j6		Michael I. Jordan, Robert A. Jacobs: Hierarchical Mixtures of Experts and the EM Algorithm. Neural Computation 6(2): 181-214 (1994)
j5		Lawrence K. Saul, Michael I. Jordan: Learning in Boltzmann Trees. Neural Computation 6(6): 1174-1184 (1994)
j4		Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation 6(6): 1185-1201 (1994)
c20		Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. COLT 1994: 13-20
c19		Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994: 284-292
c18		Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. ICML 1994: 363-370
c17		Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan: Forward dynamic models in human motor control: Psychophysical evidence. NIPS 1994: 43-50
c16		Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352
c15		Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368
c14		Lawrence K. Saul, Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442
c13		Lei Xu, Michael I. Jordan, Geoffrey E. Hinton: An Alternative Model for Mixtures of Experts. NIPS 1994: 633-640
c12		David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. NIPS 1994: 705-712
c11		Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan: Computational Structure of coordinate transformations: A generalization study. NIPS 1994: 1125-1132
	1993
j3		Robert A. Jacobs, Michael I. Jordan: Learning piecewise control strategies in a modular neural network architecture. IEEE Transactions on Systems, Man, and Cybernetics 23(2): 337-345 (1993)
c10		Michael I. Jordan, Robert A. Jacobs: Supervised Learning and Divide-and-Conquer: A Statistical Approach. ICML 1993: 159-166
c9		Zoubin Ghahramani, Michael I. Jordan: Supervised learning from incomplete data via an EM approach. NIPS 1993: 120-127
c8		Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710
c7		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
	1992
j2		Michael I. Jordan, David E. Rumelhart: Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16(3): 307-354 (1992)
c6		Daphne Bavelier, Michael I. Jordan: A Dynamical Model of Priming and Repetition Blindness. NIPS 1992: 879-886
	1991
j1		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)
c5		Michael I. Jordan, David E. Rumelhart: Internal World Models and Supervised Learning. ML 1991: 70-74
c4		Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan: Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. NIPS 1991: 191-198
c3		Michael I. Jordan, Robert A. Jacobs: Hierarchies of Adaptive Experts. NIPS 1991: 985-992
	1990
c2		Robert A. Jacobs, Michael I. Jordan: A Competitive Modular Connectionist Architecture. NIPS 1990: 767-773
	1989
c1		Michael I. Jordan, Robert A. Jacobs: Learning to Control an Unstable System with Forward Modeling. NIPS 1989: 324-331

Coauthor Index