Michael I. Jordan

	2012
239		Aleksandr Simma, Michael I. Jordan: Modeling Events with Cascades of Poisson Processes CoRR abs/1203.3516: (2012)
238		John C. Duchi, Lester W. Mackey, Michael I. Jordan: The Asymptotics of Ranking Algorithms CoRR abs/1204.1688: (2012)
237		Alexandre Bouchard-Côté, Michael I. Jordan: Optimization of Structured Mean Field Objectives CoRR abs/1205.2658: (2012)
236		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)
235		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)
	2011
234		Percy Liang, Michael I. Jordan, Dan Klein: Learning Dependency-Based Compositional Semantics. ACL 2011: 590-599
233		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
232		Alex Shyr, Trevor Darrell, Michael I. Jordan, Raquel Urtasun: Supervised hierarchical Pitman-Yor process for natural scene segmentation. CVPR 2011: 2281-2288
231		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
230		Yue Guan, Jennifer G. Dy, Michael I. Jordan: A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. ICML 2011: 1073-1080
229		Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan: Divide-and-Conquer Matrix Factorization. NIPS 2011: 1134-1142
228		Fabian L. Wauthier, Michael I. Jordan: Bayesian Bias Mitigation for Crowdsourcing. NIPS 2011: 1800-1808
227		Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic: Nonparametric Combinatorial Sequence Models. RECOMB 2011: 516-530
226		Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica: Managing data transfers in computer clusters with orchestra. SIGCOMM 2011: 98-109
225		Donghui Yan, Aiyou Chen, Michael I. Jordan: Cluster Forests CoRR abs/1104.2930: (2011)
224		Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network CoRR abs/1105.5462: (2011)
223		Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan: Divide-and-Conquer Matrix Factorization CoRR abs/1107.0789: (2011)
222		Percy Liang, Michael I. Jordan, Dan Klein: Learning Dependency-Based Compositional Semantics CoRR abs/1109.6841: (2011)
221		Brian Kulis, Michael I. Jordan: Revisiting k-means: New Algorithms via Bayesian Nonparametrics CoRR abs/1111.0352: (2011)
220		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)
219		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)
218		Zhihua Zhang, Guang Dai, Michael I. Jordan: Bayesian Generalized Kernel Mixed Models. Journal of Machine Learning Research 12: 111-139 (2011)
217		Donglin Niu, Jennifer G. Dy, Michael I. Jordan: Dimensionality Reduction for Spectral Clustering. Journal of Machine Learning Research - Proceedings Track 15: 552-560 (2011)
	2010
216		Alex Shyr, Raquel Urtasun, Michael I. Jordan: Sufficient dimension reduction for visual sequence classification. CVPR 2010: 3610-3617
215		Percy Liang, Michael I. Jordan, Dan Klein: Type-Based MCMC. HLT-NAACL 2010: 573-581
214		Daniel Ting, Ling Huang, Michael I. Jordan: An Analysis of the Convergence of Graph Laplacians. ICML 2010: 1079-1086
213		John C. Duchi, Lester W. Mackey, Michael I. Jordan: On the Consistency of Ranking Algorithms. ICML 2010: 327-334
212		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
211		Percy Liang, Michael I. Jordan, Dan Klein: Learning Programs: A Hierarchical Bayesian Approach. ICML 2010: 639-646
210		Lester W. Mackey, David Weiss, Michael I. Jordan: Mixed Membership Matrix Factorization. ICML 2010: 711-718
209		Donglin Niu, Jennifer G. Dy, Michael I. Jordan: Multiple Non-Redundant Spectral Clustering Views. ICML 2010: 831-838
208		Ariel Kleiner, Ali Rahimi, Michael I. Jordan: Random Conic Pursuit for Semidefinite Programming. NIPS 2010: 1135-1143
207		Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan: Tree-Structured Stick Breaking for Hierarchical Data. NIPS 2010: 19-27
206		Meihong Wang, Fei Sha, Michael I. Jordan: Unsupervised Kernel Dimension Reduction. NIPS 2010: 2379-2387
205		Fabian L. Wauthier, Michael I. Jordan: Heavy-Tailed Process Priors for Selective Shrinkage. NIPS 2010: 2406-2414
204		Alexandre Bouchard-Côté, Michael I. Jordan: Variational Inference over Combinatorial Spaces. NIPS 2010: 280-288
203		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
202		Aleksandr Simma, Michael I. Jordan: Modeling Events with Cascades of Poisson Processes. UAI 2010: 546-555
201		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)
200		Charles A. Sutton, Michael I. Jordan: Bayesian Inference in Queueing Networks CoRR abs/1001.3355: (2010)
199		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)
198		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)
197		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)
196		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)
195		Charles A. Sutton, Michael I. Jordan: Inference and Learning in Networks of Queues. Journal of Machine Learning Research - Proceedings Track 9: 796-803 (2010)
194		Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan: Bayesian Generalized Kernel Models. Journal of Machine Learning Research - Proceedings Track 9: 972-979 (2010)
193		Zhihua Zhang, Guang Dai, Michael I. Jordan: Matrix-Variate Dirichlet Process Mixture Models. Journal of Machine Learning Research - Proceedings Track 9: 980-987 (2010)
192		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)
191		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)
	2009
190		Percy Liang, Michael I. Jordan, Dan Klein: Learning Semantic Correspondences with Less Supervision. ACL/AFNLP 2009: 91-99
189		Zhihua Zhang, Guang Dai, Michael I. Jordan: A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) 2009: 632-647
188		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
187		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
186		Percy Liang, Michael I. Jordan, Dan Klein: Learning from measurements in exponential families. ICML 2009: 81
185		Donghui Yan, Ling Huang, Michael I. Jordan: Fast approximate spectral clustering. KDD 2009: 907-916
184		Percy Liang, Francis R. Bach, Guillaume Bouchard, Michael I. Jordan: Asymptotically Optimal Regularization in Smooth Parametric Models. NIPS 2009: 1132-1140
183		Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan: Nonparametric Latent Feature Models for Link Prediction. NIPS 2009: 1276-1284
182		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Sharing Features among Dynamical Systems with Beta Processes. NIPS 2009: 549-557
181		Michael I. Jordan: Combinatorial stochastic processes and nonparametric Bayesian modeling. SODA 2009: 139
180		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
179		Alexandre Bouchard-Côté, Michael I. Jordan: Optimization of Structured Mean Field Objectives. UAI 2009: 67-74
178		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)
177		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)
176		Zhihua Zhang, Michael I. Jordan: Latent Variable Models for Dimensionality Reduction. Journal of Machine Learning Research - Proceedings Track 5: 655-662 (2009)
	2008
175		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
174		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. ICML 2008: 312-319
173		Percy Liang, Michael I. Jordan: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. ICML 2008: 584-591
172		Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan: High-dimensional support union recovery in multivariate regression. NIPS 2008: 1217-1224
171		Erik B. Sudderth, Michael I. Jordan: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592
170		Alexandre Bouchard-Côté, Michael I. Jordan, Dan Klein: Efficient Inference in Phylogenetic InDel Trees. NIPS 2008: 177-184
169		Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS 2008: 1969-1976
168		Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464
167		Ling Huang, Donghui Yan, Michael I. Jordan, Nina Taft: Spectral Clustering with Perturbed Data. NIPS 2008: 705-712
166		Simon Lacoste-Julien, Fei Sha, Michael I. Jordan: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904
165		Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan: On the Inference of Ancestries in Admixed Populations. RECOMB 2008: 424-433
164		Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Mining Console Logs for Large-Scale System Problem Detection. SysML 2008
163		Charles A. Sutton, Michael I. Jordan: Probabilistic Inference in Queueing Networks. SysML 2008
162		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
161		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by convex risk minimization CoRR abs/0809.0853: (2008)
160		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)
159		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)
158		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)
	2007
157		Michael I. Jordan: Statistical Machine Learning and Computational Biology. BIBM 2007: 4
156		Percy Liang, Slav Petrov, Michael I. Jordan, Dan Klein: The Infinite PCFG Using Hierarchical Dirichlet Processes. EMNLP-CoNLL 2007: 688-697
155		Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8
154		Tao Li, Chris H. Q. Ding, Michael I. Jordan: Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. ICDM 2007: 577-582
153		Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124
152		Percy Liang, Michael I. Jordan, Benjamin Taskar: A permutation-augmented sampler for DP mixture models. ICML 2007: 545-552
151		Jens Nilsson, Fei Sha, Michael I. Jordan: Regression on manifolds using kernel dimension reduction. ICML 2007: 697-704
150		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
149		Percy Liang, Dan Klein, Michael I. Jordan: Agreement-Based Learning. NIPS 2007
148		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. NIPS 2007
147		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
146		Eric P. Xing, Michael I. Jordan, Roded Sharan: Bayesian Haplotype Inference via the Dirichlet Process. Journal of Computational Biology 14(3): 267-284 (2007)
145		Romain Thibaux, Michael I. Jordan: Hierarchical Beta Processes and the Indian Buffet Process. Journal of Machine Learning Research - Proceedings Track 2: 564-571 (2007)
	2006
144		Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan: Word Alignment via Quadratic Assignment. HLT-NAACL 2006
143		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
142		Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken: Statistical debugging: simultaneous identification of multiple bugs. ICML 2006: 1105-1112
141		Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner: A graphical model for predicting protein molecular function. ICML 2006: 297-304
140		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
139		Zhihua Zhang, Michael I. Jordan: Bayesian Multicategory Support Vector Machines. UAI 2006
138		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)
137		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On optimal quantization rules for some sequential decision problems CoRR abs/math/0608556: (2006)
136		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)
135		Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction, Dual Extragradient and Bregman Projections. Journal of Machine Learning Research 7: 1627-1653 (2006)
134		Francis R. Bach, Michael I. Jordan: Learning Spectral Clustering, With Application To Speech Separation. Journal of Machine Learning Research 7: 1963-2001 (2006)
133		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
132		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
131		Francis R. Bach, Michael I. Jordan: Predictive low-rank decomposition for kernel methods. ICML 2005: 33-40
130		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Divergences, surrogate loss functions and experimental design. NIPS 2005
129		Patrick Flaherty, Michael I. Jordan, Adam P. Arkin: Robust design of biological experiments. NIPS 2005
128		Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction via the Extragradient Method. NIPS 2005
127		Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan: Scalable statistical bug isolation. PLDI 2005: 15-26
126		Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille: The DLR Hierarchy of Approximate Inference. UAI 2005: 493-500
125		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)
124		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On divergences, surrogate loss functions, and decentralized detection CoRR abs/math/0510521: (2005)
123		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)
122		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
121		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
120		Mike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer: Failure Diagnosis Using Decision Trees. ICAC 2004: 36-43
119		Eric P. Xing, Roded Sharan, Michael I. Jordan: Bayesian haplo-type inference via the dirichlet process. ICML 2004
118		XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Decentralized detection and classification using kernel methods. ICML 2004
117		Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm. ICML 2004
116		David M. Blei, Michael I. Jordan: Variational methods for the Dirichlet process. ICML 2004
115		Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet: A Direct Formulation for Sparse PCA Using Semidefinite Programming. NIPS 2004
114		Francis R. Bach, Michael I. Jordan: Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004
113		Francis R. Bach, Romain Thibaux, Michael I. Jordan: Computing regularization paths for learning multiple kernels. NIPS 2004
112		Neil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004
111		Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei: Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS 2004
110		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
109		Eric P. Xing, Michael I. Jordan: Graph Partition Strategies for Generalized Mean Field Inference. UAI 2004: 602-610
108		Jon D. McAuliffe, Lior Pachter, Michael I. Jordan: Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinformatics 20(12): 1850-1860 (2004)
107		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)
106		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)
105		Francis R. Bach, Michael I. Jordan: Learning graphical models for stationary time series. IEEE Transactions on Signal Processing 52(8): 2189-2199 (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

Coauthor Index

1	Ryan Prescott Adams	[207]
2	Alexander Aiken (Alex Aiken)	[90] [91] [127] [142]
3	Hal Albelson	[236]
4	Christophe Andrieu	[84]
5	Adam P. Arkin	[125] [129] [158]
6	Francis R. Bach (Francis Bach)	[67] [73] [76] [81] [85] [94] [96] [101] [105] [113] [114] [117] [131] [134] [184]
7	David Baker	[147]
8	Richard G. Baraniuk	[220]
9	Kobus Barnard	[87]
10	Peter L. Bartlett	[82] [95] [102]
11	Andrew G. Barto	[3] [11]
12	Daphne Bavelier	[8]
13	Matthew J. Beal	[111]
14	Fernando De Bernardinis	[99]
15	Chiranjib Bhattacharyya (Chiru Bhattacharyya)	[65] [72] [74] [83] [103]
16	Tijl De Bie	[107]
17	Lukas Biewald	[132]
18	Mark D. Biggin	[233]
19	Christopher M. Bishop	[36] [44] [46] [49]
20	Mina J. Bissell	[83]
21	David M. Blei	[66] [86] [87] [89] [97] [111] [116] [133] [138] [197] [235]
22	Ben Blum	[147]
23	Peter Bodík	[132] [203] [231]
24	Guillaume Bouchard	[184]
25	Alexandre Bouchard-Côté	[170] [179] [204] [237]
26	Philip Bradley	[147]
27	Steven E. Brenner	[123] [141]
28	Eric A. Brewer	[120]
29	George Candea	[132]
30	Lawrence Carin	[220]
31	Volkan Cevher	[220]
32	Aiyou Chen	[225]
33	Mike Y. Chen	[120]
34	Mosharaf Chowdhury (N. M. Mosharaf Kabir Chowdhury)	[226]
35	David A. Cohn	[16] [33] [34]
36	Nello Cristianini	[82] [102] [107] [110]
37	Guang Dai	[189] [193] [194] [196] [218]
38	Trevor Darrell	[232]
39	Rhiju Das	[147]
40	Umeshwar Dayal	[188]
41	Minghua Deng	[110]
42	Amol Deshpande	[61]
43	Chris H. Q. Ding (Hong Q. Ding)	[154] [175] [199]
44	Tuan Dinh	[158]
45	Arnaud Doucet	[84]
46	John C. Duchi	[213] [238]
47	Roland L. Dunbrack Jr.	[192]
48	David B. Dunson	[220]
49	Pinar Duygulu	[87]
50	Jennifer G. Dy	[209] [217] [230]
51	Barbara E. Engelhardt	[123] [141]
52	Patrick Flaherty	[125] [129] [158]
53	David A. Forsyth	[87]
54	Armando Fox	[132] [164] [180] [187] [188] [203] [212] [231]
55	Emily B. Fox	[168] [174] [182] [219]
56	Michael J. Franklin (Mike Franklin)	[203] [231]
57	K. Franks	[138]
58	Nando de Freitas	[84] [87]
59	Greg Friedman	[132]
60	Kenji Fukumizu	[96] [101]
61	Archana Ganapathi	[188]
62	Minos N. Garofalakis	[61] [140] [150]
63	Zoubin Ghahramani	[10] [16] [19] [22] [29] [33] [34] [39] [43] [52] [207]
64	Laurent El Ghaoui	[65] [72] [77] [82] [102] [103] [106] [115]
65	Guri Giaever	[125]
66	L. R. Grate	[74] [83] [103]
67	Thomas L. Griffiths	[97] [162] [183] [197]
68	Yue Guan	[230]
69	Eran Halperin	[165]
70	Bernd Hamann	[233]
71	David Heckerman	[42]
72	Joseph M. Hellerstein	[150]
73	Geoffrey E. Hinton	[17]
74	Makoto Hirayama	[5]
75	Thomas Hofmann	[50]
76	John F. Houde	[47]
77	Ling Huang	[140] [150] [164] [167] [180] [185] [187] [212] [214]
78	Min-Yu Huang	[233]
79	Jonathan Hui	[132]
80	Tommi Jaakkola	[9] [13] [20] [21] [24] [27] [32] [35] [37] [40] [46] [52] [54] [224]
81	Robert A. Jacobs	[1] [2] [3] [4] [11] [12] [15]
82	Nebojsa Jojic	[227]
83	Anthony D. Joseph	[140] [150]
84	Richard M. Karp	[69] [80] [100] [104]
85	Mitsuo Kawato	[5]
86	Michael Kearns (Michael J. Kearns, Michael S. Kearns)	[51]
87	Soile V. E. Keränen	[233]
88	David Kim	[147]
89	H. Jin Kim	[98]
90	Gad Kimmel	[165]
91	Jack F. Kirsch	[201]
92	Jyri J. Kivinen	[153] [155]
93	Dan Klein	[144] [149] [156] [170] [186] [190] [211] [215] [222] [234]
94	Ariel Kleiner	[208]
95	David W. Knowles	[233]
96	Brian Kulis	[221]
97	Jochen Kumm	[125]
98	Harumi A. Kuno	[188]
99	Simon Lacoste-Julien	[128] [135] [144] [166]
100	Gert R. G. Lanckriet	[65] [72] [77] [82] [102] [106] [107] [110] [115] [117]
101	Neil D. Lawrence	[46] [49] [112] [121]
102	Helen Levine	[132]
103	Tao Li	[154] [175] [199]
104	Wu-Jun Li	[177]
105	Percy Liang	[149] [152] [156] [173] [184] [186] [190] [211] [215] [222] [234]
106	Ben Liblit	[90] [91] [127] [142]
107	Jim Lloyd	[120]
108	Paul Lukowicz	[236]
109	Jinwen Ma	[60]
110	Justin Ma	[226]
111	Lester W. Mackey	[210] [213] [223] [229] [233] [238]
112	Jon D. McAuliffe	[95] [108] [133]
113	Deborah L. McGuinness	[236]
114	Marina Meila	[31] [38] [45] [58]
115	I. Saira Mian	[74] [83] [103] [138]
116	Kurt T. Miller	[162] [183]
117	Rajib Mitra	[192]
118	F. J. Molina	[83]
119	Michael C. Mozer (Michael Mozer)	[48]
120	Kathryn E. Muratore	[123]
121	Kevin P. Murphy	[55]
122	Mayur Naik	[127] [142]
123	Sanjiv Nanda	[236]
124	Vidya Narayanan	[236]
125	Andrew Y. Ng	[56] [59] [62] [63] [64] [66] [68] [70] [78] [86] [98]
126	XuanLong Nguyen	[93] [118] [122] [124] [130] [137] [140] [148] [150] [159] [161] [198]
127	Jens Nilsson	[151]
128	Donglin Niu	[209] [217]
129	William Stafford Noble	[107] [110]
130	Guillaume Obozinski	[172] [191]
131	Lior Pachter	[108]
132	John William Paisley	[235]
133	Kayur Patel	[132]
134	David A. Patterson	[132] [164] [180] [187] [188] [203] [212] [231]
135	Slav Petrov	[156]
136	Thomas Petsche	[48]
137	John C. Platt	[121]
138	Jan Puzicha	[50]
139	Mala L. Radhakrishnan	[158]
140	D. Radisky	[83]
141	Ali Rahimi	[208]
142	Robert A. Rebres	[158]
143	A. Rizki	[83]
144	Tamara I. Roach	[158]
145	Michal Rosen-Zvi	[126]
146	David E. Rumelhart	[6] [7]
147	Stuart J. Russell	[78] [80] [88]
148	Philip N. Sabes	[30]
149	Alberto L. Sangiovanni-Vincentelli	[99]
150	Sriram Sankararaman	[165] [201]
151	Guillermo Sapiro	[220]
152	Shankar Sastry (Shankar S. Sastry)	[98]
153	Lawrence K. Saul	[14] [18] [27] [28] [32] [35] [39] [41] [52] [53] [57]
154	Terrence J. Sejnowski	[71]
155	Fei Sha	[151] [166] [201] [206]
156	Maxim V. Shapovalov	[192]
157	Roded Sharan	[119] [146]
158	Alex Shyr	[216] [232]
159	Aleksandr Simma	[202] [239]
160	Satinder P. Singh	[9] [13] [20] [21] [24]
161	Bruno Sinopoli	[122]
162	Kimmen Sjölander	[201]
163	Padhraic Smyth	[42]
164	Kyung-Ah Sohn	[143]
165	Sara A. Solla	[51]
166	Yun S. Song	[178]
167	Ion Stoica	[226]
168	Erik B. Sudderth	[153] [155] [168] [171] [174] [182] [219]
169	Charles A. Sutton	[163] [195] [200]
170	Nina Taft (Nina Taft Plotkin)	[140] [150] [167]
171	Ameet Talwalkar	[223] [229]
172	Benjamin Taskar (Ben Taskar)	[128] [135] [144] [152] [191]
173	Sekhar Tatikonda	[75]
174	Yee Whye Teh	[111] [143]
175	Joshua B. Tenenbaum	[97]
176	Romain Thibaux	[113] [145]
177	Daniel Ting	[192] [214]
178	Emanuel Todorov	[79]
179	Gilman Tolle	[132]
180	Beth Trushkowsky	[231]
181	Raquel Urtasun	[216] [232]
182	Eric Vatikiotis-Bateson	[5]
183	Martin J. Wainwright	[92] [118] [124] [130] [136] [137] [148] [159] [160] [161] [172] [198]
184	Michael B. Wakin	[220]
185	Donghui Wang	[194]
186	Guoli Wang	[192]
187	Meihong Wang	[206]
188	Fabian L. Wauthier	[205] [227] [228]
189	Gunther H. Weber	[233]
190	David Weiss	[210]
191	Yair Weiss	[55] [63]
192	Janet L. Wiener	[188]
193	Alan S. Willsky	[168] [174] [182] [219]
194	Daniel M. Wolpert	[19] [22]
195	Wei Wu	[100] [104]
196	Eric P. Xing	[69] [78] [80] [88] [100] [104] [109] [119] [143] [146]
197	Congfu Xu	[196]
198	Lei Xu	[17] [26] [60]
199	Wei Xu	[164] [180] [187] [212]
200	Donghui Yan	[167] [185] [225]
201	Dit-Yan Yeung	[169] [177]
202	Junming Yin	[178]
203	Alan L. Yuille	[126]
204	Matei Zaharia	[226]
205	Zhihua Zhang	[139] [169] [176] [177] [189] [193] [194] [196] [218]
206	Alice X. Zheng	[62] [68] [90] [91] [120] [127] [142]
207	Alexandre d'Aspremont	[106] [115]

Colors in the list of coauthors

Last update Sat Jun 2 20:57:36 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page