![]() | 2010 | |
---|---|---|
180 | ![]() ![]() ![]() ![]() ![]() ![]() | Charles A. Sutton, Michael I. Jordan: Bayesian Inference in Queueing Networks CoRR abs/1001.3355: (2010) |
179 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Thomas L. Griffiths, Michael I. Jordan: The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57(2): (2010) |
2009 | ||
178 | ![]() ![]() ![]() ![]() ![]() ![]() | Zhihua Zhang, Guang Dai, Michael I. Jordan: A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) 2009: 632-647 |
177 | ![]() ![]() ![]() ![]() ![]() ![]() | Archana Ganapathi, Harumi A. Kuno, Umeshwar Dayal, Janet L. Wiener, Armando Fox, Michael I. Jordan, David A. Patterson: Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning. ICDE 2009: 592-603 |
176 | ![]() ![]() ![]() ![]() ![]() ![]() | Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael I. Jordan: Online System Problem Detection by Mining Patterns of Console Logs. ICDM 2009: 588-597 |
175 | ![]() ![]() ![]() ![]() ![]() ![]() | Percy Liang, Michael I. Jordan, Dan Klein: Learning from measurements in exponential families. ICML 2009: 81 |
174 | ![]() ![]() ![]() ![]() ![]() ![]() | Donghui Yan, Ling Huang, Michael I. Jordan: Fast approximate spectral clustering. KDD 2009: 907-916 |
173 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan: Combinatorial stochastic processes and nonparametric Bayesian modeling. SODA 2009: 139 |
172 | ![]() ![]() ![]() ![]() ![]() ![]() | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Detecting large-scale system problems by mining console logs. SOSP 2009: 117-132 |
171 | ![]() ![]() ![]() ![]() ![]() ![]() | Junming Yin, Michael I. Jordan, Yun S. Song: Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. Bioinformatics 25(12): (2009) |
2008 | ||
170 | ![]() ![]() ![]() ![]() ![]() ![]() | Chris H. Q. Ding, Tao Li, Michael I. Jordan: Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding. ICDM 2008: 183-192 |
169 | ![]() ![]() ![]() ![]() ![]() ![]() | Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. ICML 2008: 312-319 |
168 | ![]() ![]() ![]() ![]() ![]() ![]() | Percy Liang, Michael I. Jordan: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. ICML 2008: 584-591 |
167 | ![]() ![]() ![]() ![]() ![]() ![]() | Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan: High-dimensional support union recovery in multivariate regression. NIPS 2008: 1217-1224 |
166 | ![]() ![]() ![]() ![]() ![]() ![]() | Erik B. Sudderth, Michael I. Jordan: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592 |
165 | ![]() ![]() ![]() ![]() ![]() ![]() | Alexandre Bouchard-Côté, Michael I. Jordan, Dan Klein: Efficient Inference in Phylogenetic InDel Trees. NIPS 2008: 177-184 |
164 | ![]() ![]() ![]() ![]() ![]() ![]() | Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS 2008: 1969-1976 |
163 | ![]() ![]() ![]() ![]() ![]() ![]() | Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464 |
162 | ![]() ![]() ![]() ![]() ![]() ![]() | Ling Huang, Donghui Yan, Michael I. Jordan, Nina Taft: Spectral Clustering with Perturbed Data. NIPS 2008: 705-712 |
161 | ![]() ![]() ![]() ![]() ![]() ![]() | Simon Lacoste-Julien, Fei Sha, Michael I. Jordan: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904 |
160 | ![]() ![]() ![]() ![]() ![]() ![]() | Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan: On the Inference of Ancestries in Admixed Populations. RECOMB 2008: 424-433 |
159 | ![]() ![]() ![]() ![]() ![]() ![]() | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan: Mining Console Logs for Large-Scale System Problem Detection. SysML 2008 |
158 | ![]() ![]() ![]() ![]() ![]() ![]() | Charles A. Sutton, Michael I. Jordan: Probabilistic Inference in Queueing Networks. SysML 2008 |
157 | ![]() ![]() ![]() ![]() ![]() ![]() | Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan: The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. UAI 2008: 403-410 |
156 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by convex risk minimization CoRR abs/0809.0853: (2008) |
155 | ![]() ![]() ![]() ![]() ![]() ![]() | Martin J. Wainwright, Michael I. Jordan: Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning 1(1-2): 1-305 (2008) |
154 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection. IEEE Transactions on Information Theory 54(7): 3285-3295 (2008) |
2007 | ||
153 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan: Statistical Machine Learning and Computational Biology. BIBM 2007: 4 |
152 | ![]() ![]() ![]() ![]() ![]() ![]() | Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8 |
151 | ![]() ![]() ![]() ![]() ![]() ![]() | Tao Li, Chris H. Q. Ding, Michael I. Jordan: Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. ICDM 2007: 577-582 |
150 | ![]() ![]() ![]() ![]() ![]() ![]() | Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124 |
149 | ![]() ![]() ![]() ![]() ![]() ![]() | Percy Liang, Michael I. Jordan, Benjamin Taskar: A permutation-augmented sampler for DP mixture models. ICML 2007: 545-552 |
148 | ![]() ![]() ![]() ![]() ![]() ![]() | Jens Nilsson, Fei Sha, Michael I. Jordan: Regression on manifolds using kernel dimension reduction. ICML 2007: 697-704 |
147 | ![]() ![]() ![]() ![]() ![]() ![]() | Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Nina Taft: Communication-Efficient Online Detection of Network-Wide Anomalies. INFOCOM 2007: 134-142 |
146 | ![]() ![]() ![]() ![]() ![]() ![]() | Percy Liang, Dan Klein, Michael I. Jordan: Agreement-Based Learning. NIPS 2007 |
145 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. NIPS 2007 |
144 | ![]() ![]() ![]() ![]() ![]() ![]() | Ben Blum, Michael I. Jordan, David Kim, Rhiju Das, Philip Bradley, David Baker: Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. NIPS 2007 |
143 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Michael I. Jordan, Roded Sharan: Bayesian Haplotype Inference via the Dirichlet Process. Journal of Computational Biology 14(3): 267-284 (2007) |
2006 | ||
142 | ![]() ![]() ![]() ![]() ![]() ![]() | Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan: Word Alignment via Quadratic Assignment. HLT-NAACL 2006 |
141 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh: Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. ICML 2006: 1049-1056 |
140 | ![]() ![]() ![]() ![]() ![]() ![]() | Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken: Statistical debugging: simultaneous identification of multiple bugs. ICML 2006: 1105-1112 |
139 | ![]() ![]() ![]() ![]() ![]() ![]() | Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner: A graphical model for predicting protein molecular function. ICML 2006: 297-304 |
138 | ![]() ![]() ![]() ![]() ![]() ![]() | Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Michael I. Jordan, Anthony D. Joseph, Nina Taft: In-Network PCA and Anomaly Detection. NIPS 2006: 617-624 |
137 | ![]() ![]() ![]() ![]() ![]() ![]() | Zhihua Zhang, Michael I. Jordan: Bayesian Multicategory Support Vector Machines. UAI 2006 |
136 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, K. Franks, Michael I. Jordan, I. Saira Mian: Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. BMC Bioinformatics 7: 250 (2006) |
135 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On optimal quantization rules for some sequential decision problems CoRR abs/math/0608556: (2006) |
134 | ![]() ![]() ![]() ![]() ![]() ![]() | Martin J. Wainwright, Michael I. Jordan: Log-determinant relaxation for approximate inference in discrete Markov random fields. IEEE Transactions on Signal Processing 54(6-1): 2099-2109 (2006) |
133 | ![]() ![]() ![]() ![]() ![]() ![]() | Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction, Dual Extragradient and Bregman Projections. Journal of Machine Learning Research 7: 1627-1653 (2006) |
132 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Learning Spectral Clustering, With Application To Speech Separation. Journal of Machine Learning Research 7: 1963-2001 (2006) |
131 | ![]() ![]() ![]() ![]() ![]() ![]() | Jon D. McAuliffe, David M. Blei, Michael I. Jordan: Nonparametric empirical Bayes for the Dirichlet process mixture model. Statistics and Computing 16(1): 5-14 (2006) |
2005 | ||
130 | ![]() ![]() ![]() ![]() ![]() ![]() | Peter Bodík, Greg Friedman, Lukas Biewald, Helen Levine, George Candea, Kayur Patel, Gilman Tolle, Jonathan Hui, Armando Fox, Michael I. Jordan, David A. Patterson: Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization. ICAC 2005: 89-100 |
129 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Predictive low-rank decomposition for kernel methods. ICML 2005: 33-40 |
128 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Divergences, surrogate loss functions and experimental design. NIPS 2005 |
127 | ![]() ![]() ![]() ![]() ![]() ![]() | Patrick Flaherty, Michael I. Jordan, Adam P. Arkin: Robust design of biological experiments. NIPS 2005 |
126 | ![]() ![]() ![]() ![]() ![]() ![]() | Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan: Structured Prediction via the Extragradient Method. NIPS 2005 |
125 | ![]() ![]() ![]() ![]() ![]() ![]() | Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan: Scalable statistical bug isolation. PLDI 2005: 15-26 |
124 | ![]() ![]() ![]() ![]() ![]() ![]() | Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille: The DLR Hierarchy of Approximate Inference. UAI 2005: 493-500 |
123 | ![]() ![]() ![]() ![]() ![]() ![]() | Patrick Flaherty, Guri Giaever, Jochen Kumm, Michael I. Jordan, Adam P. Arkin: A latent variable model for chemogenomic profiling. Bioinformatics 21(15): 3286-3293 (2005) |
122 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: On divergences, surrogate loss functions, and decentralized detection CoRR abs/math/0510521: (2005) |
121 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Michael I. Jordan, Bruno Sinopoli: A kernel-based learning approach to ad hoc sensor network localization. TOSN 1(1): 134-152 (2005) |
2004 | ||
120 | ![]() ![]() ![]() ![]() ![]() ![]() | Neil D. Lawrence, John C. Platt, Michael I. Jordan: Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning 2004: 56-87 |
119 | ![]() ![]() ![]() ![]() ![]() ![]() | Mike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer: Failure Diagnosis Using Decision Trees. ICAC 2004: 36-43 |
118 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Roded Sharan, Michael I. Jordan: Bayesian haplo-type inference via the dirichlet process. ICML 2004 |
117 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Decentralized detection and classification using kernel methods. ICML 2004 |
116 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm. ICML 2004 |
115 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Michael I. Jordan: Variational methods for the Dirichlet process. ICML 2004 |
114 | ![]() ![]() ![]() ![]() ![]() ![]() | Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet: A Direct Formulation for Sparse PCA Using Semidefinite Programming. NIPS 2004 |
113 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004 |
112 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Romain Thibaux, Michael I. Jordan: Computing regularization paths for learning multiple kernels. NIPS 2004 |
111 | ![]() ![]() ![]() ![]() ![]() ![]() | Neil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004 |
110 | ![]() ![]() ![]() ![]() ![]() ![]() | Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei: Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS 2004 |
109 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble: Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. Pacific Symposium on Biocomputing 2004: 300-311 |
108 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Michael I. Jordan: Graph Partition Strategies for Generalized Mean Field Inference. UAI 2004: 602-610 |
107 | ![]() ![]() ![]() ![]() ![]() ![]() | Jon D. McAuliffe, Lior Pachter, Michael I. Jordan: Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinformatics 20(12): 1850-1860 (2004) |
106 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble: A statistical framework for genomic data fusion. Bioinformatics 20(16): 2626-2635 (2004) |
105 | ![]() ![]() ![]() ![]() ![]() ![]() | Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet: A direct formulation for sparse PCA using semidefinite programming CoRR cs.CE/0406021: (2004) |
104 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp: Logos: a Modular Bayesian Model for de Novo Motif Detection. J. Bioinformatics and Computational Biology 2(1): 127-154 (2004) |
103 | ![]() ![]() ![]() ![]() ![]() ![]() | Chiranjib Bhattacharyya, L. R. Grate, Michael I. Jordan, Laurent El Ghaoui, I. Saira Mian: Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data. Journal of Computational Biology 11(6): 1073-1089 (2004) |
102 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5: 27-72 (2004) |
101 | ![]() ![]() ![]() ![]() ![]() ![]() | Kenji Fukumizu, Francis R. Bach, Michael I. Jordan: Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. Journal of Machine Learning Research 5: 73-99 (2004) |
2003 | ||
100 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp: LOGOS: a modular Bayesian model for de novo motif detection. CSB 2003: 266-276 |
99 | ![]() ![]() ![]() ![]() ![]() ![]() | Fernando De Bernardinis, Michael I. Jordan, Alberto L. Sangiovanni-Vincentelli: Support vector machines for analog circuit performance representation. DAC 2003: 964-969 |
98 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry: Autonomous Helicopter Flight via Reinforcement Learning. NIPS 2003 |
97 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum: Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003 |
96 | ![]() ![]() ![]() ![]() ![]() ![]() | Kenji Fukumizu, Francis R. Bach, Michael I. Jordan: Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003 |
95 | ![]() ![]() ![]() ![]() ![]() ![]() | Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003 |
94 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Learning Spectral Clustering. NIPS 2003 |
93 | ![]() ![]() ![]() ![]() ![]() ![]() | XuanLong Nguyen, Michael I. Jordan: On the Concentration of Expectation and Approximate Inference in Layered Networks. NIPS 2003 |
92 | ![]() ![]() ![]() ![]() ![]() ![]() | Martin J. Wainwright, Michael I. Jordan: Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. NIPS 2003 |
91 | ![]() ![]() ![]() ![]() ![]() ![]() | Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alexander Aiken: Statistical Debugging of Sampled Programs. NIPS 2003 |
90 | ![]() ![]() ![]() ![]() ![]() ![]() | Ben Liblit, Alexander Aiken, Alice X. Zheng, Michael I. Jordan: Bug isolation via remote program sampling. PLDI 2003: 141-154 |
89 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Michael I. Jordan: Modeling annotated data. SIGIR 2003: 127-134 |
88 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Michael I. Jordan, Stuart J. Russell: A generalized mean field algorithm for variational inference in exponential families. UAI 2003: 583-591 |
87 | ![]() ![]() ![]() ![]() ![]() ![]() | Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan: Matching Words and Pictures. Journal of Machine Learning Research 3: 1107-1135 (2003) |
86 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993-1022 (2003) |
85 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Beyond Independent Components: Trees and Clusters. Journal of Machine Learning Research 4: 1205-1233 (2003) |
84 | ![]() ![]() ![]() ![]() ![]() ![]() | Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan: An Introduction to MCMC for Machine Learning. Machine Learning 50(1-2): 5-43 (2003) |
83 | ![]() ![]() ![]() ![]() ![]() ![]() | Chiranjib Bhattacharyya, L. R. Grate, A. Rizki, D. Radisky, F. J. Molina, Michael I. Jordan, Mina J. Bissell, I. Saira Mian: Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. Signal Processing 83(4): 729-743 (2003) |
2002 | ||
82 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan: Learning the Kernel Matrix with Semi-Definite Programming. ICML 2002: 323-330 |
81 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Learning Graphical Models with Mercer Kernels. NIPS 2002: 1009-1016 |
80 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell: A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. NIPS 2002: 1489-1496 |
79 | ![]() ![]() ![]() ![]() ![]() ![]() | Emanuel Todorov, Michael I. Jordan: A Minimal Intervention Principle for Coordinated Movement. NIPS 2002: 27-34 |
78 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell: Distance Metric Learning with Application to Clustering with Side-Information. NIPS 2002: 505-512 |
77 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan: Robust Novelty Detection with Single-Class MPM. NIPS 2002: 905-912 |
76 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Tree-dependent Component Analysis. UAI 2002: 36-44 |
75 | ![]() ![]() ![]() ![]() ![]() ![]() | Sekhar Tatikonda, Michael I. Jordan: Loopy Belief Propogation and Gibbs Measures. UAI 2002: 493-500 |
74 | ![]() ![]() ![]() ![]() ![]() ![]() | L. R. Grate, Chiranjib Bhattacharyya, Michael I. Jordan, I. Saira Mian: Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. WABI 2002: 1-9 |
73 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Kernel Independent Component Analysis. Journal of Machine Learning Research 3: 1-48 (2002) |
72 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan: A Robust Minimax Approach to Classification. Journal of Machine Learning Research 3: 555-582 (2002) |
71 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Terrence J. Sejnowski: Graphical Models: Foundations of Neural Computation. Pattern Anal. Appl. 5(4): 401-402 (2002) |
2001 | ||
70 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Michael I. Jordan: Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001: 377-384 |
69 | ![]() ![]() ![]() ![]() ![]() ![]() | Eric P. Xing, Michael I. Jordan, Richard M. Karp: Feature selection for high-dimensional genomic microarray data. ICML 2001: 601-608 |
68 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan: Link Analysis, Eigenvectors and Stability. IJCAI 2001: 903-910 |
67 | ![]() ![]() ![]() ![]() ![]() ![]() | Francis R. Bach, Michael I. Jordan: Thin Junction Trees. NIPS 2001: 569-576 |
66 | ![]() ![]() ![]() ![]() ![]() ![]() | David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. NIPS 2001: 601-608 |
65 | ![]() ![]() ![]() ![]() ![]() ![]() | Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan: Minimax Probability Machine. NIPS 2001: 801-807 |
64 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Michael I. Jordan: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001: 841-848 |
63 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Michael I. Jordan, Yair Weiss: On Spectral Clustering: Analysis and an algorithm. NIPS 2001: 849-856 |
62 | ![]() ![]() ![]() ![]() ![]() ![]() | Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan: Stable Algorithms for Link Analysis. SIGIR 2001: 258-266 |
61 | ![]() ![]() ![]() ![]() ![]() ![]() | Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan: Efficient Stepwise Selection in Decomposable Models. UAI 2001: 128-135 |
60 | ![]() ![]() ![]() ![]() ![]() ![]() | Jinwen Ma, Lei Xu, Michael I. Jordan: Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. Neural Computation 12(12): 2881-2907 (2001) |
2000 | ||
59 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Michael I. Jordan: PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000: 406-415 |
58 | ![]() ![]() ![]() ![]() ![]() ![]() | Marina Meila, Michael I. Jordan: Learning with Mixtures of Trees. Journal of Machine Learning Research 1: 1-48 (2000) |
57 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12(6): 1313-1335 (2000) |
1999 | ||
56 | ![]() ![]() ![]() ![]() ![]() ![]() | Andrew Y. Ng, Michael I. Jordan: Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999: 533-539 |
55 | ![]() ![]() ![]() ![]() ![]() ![]() | Kevin P. Murphy, Yair Weiss, Michael I. Jordan: Loopy Belief Propagation for Approximate Inference: An Empirical Study. UAI 1999: 467-475 |
54 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. (JAIR) 10: 291-322 (1999) |
53 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Machine Learning 37(1): 75-87 (1999) |
52 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola, Lawrence K. Saul: An Introduction to Variational Methods for Graphical Models. Machine Learning 37(2): 183-233 (1999) |
1998 | ||
51 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Michael J. Kearns, Sara A. Solla: Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997] The MIT Press 1998 |
50 | ![]() ![]() ![]() ![]() ![]() ![]() | Thomas Hofmann, Jan Puzicha, Michael I. Jordan: Learning from Dyadic Data. NIPS 1998: 466-472 |
49 | ![]() ![]() ![]() ![]() ![]() ![]() | Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327 |
1997 | ||
48 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael Mozer, Michael I. Jordan, Thomas Petsche: Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996 MIT Press 1997 |
47 | ![]() ![]() ![]() ![]() ![]() ![]() | John F. Houde, Michael I. Jordan: Adaptation in Speech Motor Control. NIPS 1997 |
46 | ![]() ![]() ![]() ![]() ![]() ![]() | Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997 |
45 | ![]() ![]() ![]() ![]() ![]() ![]() | Marina Meila, Michael I. Jordan: Estimating Dependency Structure as a Hidden Variable. NIPS 1997 |
44 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Christopher M. Bishop: Neural Networks. The Computer Science and Engineering Handbook 1997: 536-556 |
43 | ![]() ![]() ![]() ![]() ![]() ![]() | Zoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. Machine Learning 29(2-3): 245-273 (1997) |
42 | ![]() ![]() ![]() ![]() ![]() ![]() | Padhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997) |
1996 | ||
41 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996: 267-273 |
40 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Michael I. Jordan: Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493 |
39 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul: Hidden Markov Decision Trees. NIPS 1996: 501-507 |
38 | ![]() ![]() ![]() ![]() ![]() ![]() | Marina Meila, Michael I. Jordan: Triangulation by Continuous Embedding. NIPS 1996: 557-563 |
37 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Michael I. Jordan: Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996: 340-348 |
36 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Christopher M. Bishop: Neural Networks. ACM Comput. Surv. 28(1): 73-75 (1996) |
35 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks CoRR cs.AI/9603102: (1996) |
34 | ![]() ![]() ![]() ![]() ![]() ![]() | David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models CoRR cs.AI/9603104: (1996) |
33 | ![]() ![]() ![]() ![]() ![]() ![]() | David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. J. Artif. Intell. Res. (JAIR) 4: 129-145 (1996) |
32 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996) |
1995 | ||
31 | ![]() ![]() ![]() ![]() ![]() ![]() | Marina Meila, Michael I. Jordan: Learning Fine Motion by Markov Mixtures of Experts. NIPS 1995: 1003-1009 |
30 | ![]() ![]() ![]() ![]() ![]() ![]() | Philip N. Sabes, Michael I. Jordan: Reinforcement Learning by Probability Matching. NIPS 1995: 1080-1086 |
29 | ![]() ![]() ![]() ![]() ![]() ![]() | Zoubin Ghahramani, Michael I. Jordan: Factorial Hidden Markov Models. NIPS 1995: 472-478 |
28 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492 |
27 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534 |
26 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Lei Xu: Convergence results for the EM approach to mixtures of experts architectures. Neural Networks 8(9): 1409-1431 (1995) |
1994 | ||
25 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. COLT 1994: 13-20 |
24 | ![]() ![]() ![]() ![]() ![]() ![]() | Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994: 284-292 |
23 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. ICML 1994: 363-370 |
22 | ![]() ![]() ![]() ![]() ![]() ![]() | Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan: Computational Structure of coordinate transformations: A generalization study. NIPS 1994: 1125-1132 |
21 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352 |
20 | ![]() ![]() ![]() ![]() ![]() ![]() | Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan: Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368 |
19 | ![]() ![]() ![]() ![]() ![]() ![]() | Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan: Forward dynamic models in human motor control: Psychophysical evidence. NIPS 1994: 43-50 |
18 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442 |
17 | ![]() ![]() ![]() ![]() ![]() ![]() | Lei Xu, Michael I. Jordan, Geoffrey E. Hinton: An Alternative Model for Mixtures of Experts. NIPS 1994: 633-640 |
16 | ![]() ![]() ![]() ![]() ![]() ![]() | David A. Cohn, Zoubin Ghahramani, Michael I. Jordan: Active Learning with Statistical Models. NIPS 1994: 705-712 |
15 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Robert A. Jacobs: Hierarchical Mixtures of Experts and the EM Algorithm. Neural Computation 6(2): 181-214 (1994) |
14 | ![]() ![]() ![]() ![]() ![]() ![]() | Lawrence K. Saul, Michael I. Jordan: Learning in Boltzmann Trees. Neural Computation 6(6): 1174-1184 (1994) |
13 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation 6(6): 1185-1201 (1994) |
1993 | ||
12 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Robert A. Jacobs: Supervised Learning and Divide-and-Conquer: A Statistical Approach. ICML 1993: 159-166 |
11 | ![]() ![]() ![]() ![]() ![]() ![]() | Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto: Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Machine Learning: From Theory to Applications 1993: 175-202 |
10 | ![]() ![]() ![]() ![]() ![]() ![]() | Zoubin Ghahramani, Michael I. Jordan: Supervised learning from incomplete data via an EM approach. NIPS 1993: 120-127 |
9 | ![]() ![]() ![]() ![]() ![]() ![]() | Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710 |
1992 | ||
8 | ![]() ![]() ![]() ![]() ![]() ![]() | Daphne Bavelier, Michael I. Jordan: A Dynamical Model of Priming and Repetition Blindness. NIPS 1992: 879-886 |
7 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, David E. Rumelhart: Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16(3): 307-354 (1992) |
1991 | ||
6 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, David E. Rumelhart: Internal World Models and Supervised Learning. ML 1991: 70-74 |
5 | ![]() ![]() ![]() ![]() ![]() ![]() | Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan: Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. NIPS 1991: 191-198 |
4 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Robert A. Jacobs: Hierarchies of Adaptive Experts. NIPS 1991: 985-992 |
3 | ![]() ![]() ![]() ![]() ![]() ![]() | Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto: Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cognitive Science 15(2): 219-250 (1991) |
1990 | ||
2 | ![]() ![]() ![]() ![]() ![]() ![]() | Robert A. Jacobs, Michael I. Jordan: A Competitive Modular Connectionist Architecture. NIPS 1990: 767-773 |
1989 | ||
1 | ![]() ![]() ![]() ![]() ![]() ![]() | Michael I. Jordan, Robert A. Jacobs: Learning to Control an Unstable System with Forward Modeling. NIPS 1989: 324-331 |