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All publications by Michael I. Jordan
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Variational Consensus Monte Carlo
Maxim Rabinovich, Elaine Angelino and Michael I. Jordan
Advances in Neural Information Processing Systems 28, 2015


Parallel Correlation Clustering on Big Graphs
Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran and Michael I. Jordan
Advances in Neural Information Processing Systems 28, 2015


On the Accuracy of Self-Normalized Log-Linear Models
Jacob Andreas, Maxim Rabinovich, Michael I. Jordan and Dan Klein
Advances in Neural Information Processing Systems 28, 2015


Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Ryan J. Giordano, Tamara Broderick and Michael I. Jordan
Advances in Neural Information Processing Systems 28, 2015


Communication-Efficient Distributed Dual Coordinate Ascent
Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann and Michael I. Jordan
Advances in Neural Information Processing Systems 27, 2014


Parallel Double Greedy Submodular Maximization
Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley and Michael I. Jordan
Advances in Neural Information Processing Systems 27, 2014


Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou and Michael I. Jordan
Advances in Neural Information Processing Systems 27, 2014


On the Convergence Rate of Decomposable Submodular Function Minimization
Robert Nishihara, Stefanie Jegelka and Michael I. Jordan
Advances in Neural Information Processing Systems 27, 2014


Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models
Ke Jiang, Brian Kulis and Michael I. Jordan
Advances in Neural Information Processing Systems 25, 2012


Ancestor Sampling for Particle Gibbs
Fredrik Lindsten, Thomas Schön and Michael I. Jordan
Advances in Neural Information Processing Systems 25, 2012


Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods
Andre Wibisono, Martin J. Wainwright, Michael I. Jordan and John C. Duchi
Advances in Neural Information Processing Systems 25, 2012


Privacy Aware Learning
Martin J. Wainwright, Michael I. Jordan and John C. Duchi
Advances in Neural Information Processing Systems 25, 2012


Nonparametric Link Prediction in Dynamic Networks
Purnamrita Sarkar, Deepayan Chakrabarti and Michael I. Jordan
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Variational Bayesian Inference with Stochastic Search
David M. Blei, Michael I. Jordan and John W. Paisley
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Brian Kulis and Michael I. Jordan
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


The Big Data Bootstrap
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar and Michael I. Jordan
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Stick-Breaking Beta Processes and the Poisson Process
John W. Paisley, David M. Blei and Michael I. Jordan
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Bayesian Bias Mitigation for Crowdsourcing
Fabian L. Wauthier and Michael I. Jordan
Advances in Neural Information Processing Systems 24, 2011


Divide-and-Conquer Matrix Factorization
Lester W. Mackey, Michael I. Jordan and Ameet Talwalkar
Advances in Neural Information Processing Systems 24, 2011


A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection
Yue Guan, Michael I. Jordan and Jennifer G. Dy
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Dimensionality Reduction for Spectral Clustering
Donglin Niu, Jennifer G. Dy and Michael I. Jordan
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Detecting Large-Scale System Problems by Mining Console Logs
Wei Xu, Ling Huang, Armando Fox, David A. Patterson and Michael I. Jordan
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


An Analysis of the Convergence of Graph Laplacians
Daniel Ting, Ling Huang and Michael I. Jordan
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Multiple Non-Redundant Spectral Clustering Views
Donglin Niu, Jennifer G. Dy and Michael I. Jordan
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Mixed Membership Matrix Factorization
Lester W. Mackey, David Weiss and Michael I. Jordan
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Learning Programs: A Hierarchical Bayesian Approach
Percy Liang, Michael I. Jordan and Dan Klein
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


On the Consistency of Ranking Algorithms
John C. Duchi, Lester W. Mackey and Michael I. Jordan
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Random Conic Pursuit for Semidefinite Programming
Ariel Kleiner, Ali Rahimi and Michael I. Jordan
Advances in Neural Information Processing Systems 23, 2010


Unsupervised Kernel Dimension Reduction
Meihong Wang, Fei Sha and Michael I. Jordan
Advances in Neural Information Processing Systems 23, 2010


Heavy-Tailed Process Priors for Selective Shrinkage
Fabian L. Wauthier and Michael I. Jordan
Advances in Neural Information Processing Systems 23, 2010


Variational Inference over Combinatorial Spaces
Alexandre Bouchard-côté and Michael I. Jordan
Advances in Neural Information Processing Systems 23, 2010


Tree-Structured Stick Breaking for Hierarchical Data
Zoubin Ghahramani, Michael I. Jordan and Ryan P. Adams
Advances in Neural Information Processing Systems 23, 2010


Bayesian Generalized Kernel Models
Zhihua Zhang, Guang Dai, Donghui Wang and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Matrix-Variate Dirichlet Process Mixture Models
Zhihua Zhang, Guang Dai and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Inference and Learning in Networks of Queues
Charles A. Sutton and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning from measurements in exponential families
Percy Liang, Michael I. Jordan and Dan Klein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Nonparametric Latent Feature Models for Link Prediction
Kurt Miller, Michael I. Jordan and Thomas L. Griffiths
Advances in Neural Information Processing Systems 22, 2009


Asymptotically Optimal Regularization in Smooth Parametric Models
Percy Liang, Guillaume Bouchard, Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 22, 2009


Sharing Features among Dynamical Systems with Beta Processes
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan and Alan S. Willsky
Advances in Neural Information Processing Systems 22, 2009


Coherence Functions for Multicategory Margin-based Classification Methods
Zhihua Zhang, Michael I. Jordan, Wu-jun Li and Dit-yan Yeung
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Latent Variable Models for Dimensionality Reduction
Zhihua Zhang and Michael I. Jordan
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Percy Liang and Michael I. Jordan
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


An HDP-HMM for systems with state persistence
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan and Alan S. Willsky
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
Erik B. Sudderth and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
Simon Lacoste-julien, Fei Sha and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


High-dimensional support union recovery in multivariate regression
Guillaume R. Obozinski, Martin J. Wainwright and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


Spectral Clustering with Perturbed Data
Ling Huang, Donghui Yan, Nina Taft and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


Efficient Inference in Phylogenetic InDel Trees
Alexandre Bouchard-côté, Dan Klein and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Emily B. Fox, Erik B. Sudderth, Alan S. Willsky, Michael I. Jordan and Alan S. Willsky
Advances in Neural Information Processing Systems 21, 2008


Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
Zhihua Zhang, Michael I. Jordan and Dit-yan Yeung
Advances in Neural Information Processing Systems 21, 2008


Regression on manifolds using kernel dimension reduction
Jens Nilsson, Fei Sha and Michael I. Jordan
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


A permutation-augmented sampler for DP mixture models
Percy Liang, Michael I. Jordan and Ben Taskar
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
Xuanlong Nguyen, Martin J. Wainwright and Michael I. Jordan
Advances in Neural Information Processing Systems 20, 2007


Agreement-Based Learning
Percy Liang, Dan Klein and Michael I. Jordan
Advances in Neural Information Processing Systems 20, 2007


Feature Selection Methods for Improving Protein Structure Prediction with Rosetta
Ben Blum, Rhiju Das, Philip Bradley, David Baker, Michael I. Jordan and David Tax
Advances in Neural Information Processing Systems 20, 2007


Hierarchical Beta Processes and the Indian Buffet Process
Romain Thibaux and Michael I. Jordan
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Statistical debugging: simultaneous identification of multiple bugs
Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik and Alex Aiken
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture
Eric P. Xing, Kyung-ah Sohn, Michael I. Jordan and Yee W. Teh
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


A graphical model for predicting protein molecular function
Barbara E. Engelhardt, Michael I. Jordan and Steven E. Brenner
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Structured Prediction, Dual Extragradient and Bregman Projections
Benjamin Taskar, Simon Lacoste-julien and Michael I. Jordan
Journal of Machine Learning Research, 2006


In-Network PCA and Anomaly Detection
Ling Huang, Xuanlong Nguyen, Minos Garofalakis, Michael I. Jordan, Anthony Joseph and Nina Taft
Advances in Neural Information Processing Systems 19, 2006


Learning Spectral Clustering, With Application To Speech Separation
Francis R. Bach and Michael I. Jordan
Journal of Machine Learning Research, 2006


Predictive low-rank decomposition for kernel methods
Francis R. Bach and Michael I. Jordan
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Robust design of biological experiments
Patrick Flaherty, Adam Arkin and Michael I. Jordan
Advances in Neural Information Processing Systems 18, 2005


Bayesian haplo-type inference via the dirichlet process
Eric P. Xing, Roded Sharan and Michael I. Jordan
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Decentralized detection and classification using kernel methods
Xuanlong Nguyen, Martin J. Wainwright and Michael I. Jordan
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Variational methods for the Dirichlet process
David M. Blei and Michael I. Jordan
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Multiple kernel learning, conic duality, and the SMO algorithm
Francis R. Bach, Gert Lanckriet and Michael I. Jordan
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Computing regularization paths for learning multiple kernels
Francis R. Bach, Romain Thibaux and Michael I. Jordan
Advances in Neural Information Processing Systems 17, 2004


Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
Yee W. Teh, Michael I. Jordan, Matthew J. Beal and David M. Blei
Advances in Neural Information Processing Systems 17, 2004


A Direct Formulation for Sparse PCA Using Semidefinite Programming
Alexandre D'aspremont, Laurent E. Ghaoui, Michael I. Jordan and Gert R. Lanckriet
Advances in Neural Information Processing Systems 17, 2004


Blind One-microphone Speech Separation: A Spectral Learning Approach
Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 17, 2004


Semi-supervised Learning via Gaussian Processes
Neil D. Lawrence and Michael I. Jordan
Advances in Neural Information Processing Systems 17, 2004


Learning the Kernel Matrix with Semidefinite Programming
Gert Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent E. Ghaoui and Michael I. Jordan
Journal of Machine Learning Research, 2004


Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
Kenji Fukumizu, Francis R. Bach and Michael I. Jordan
Journal of Machine Learning Research, 2004


Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates
Peter L. Bartlett, Michael I. Jordan and Jon D. Mcauliffe
Advances in Neural Information Processing Systems 16, 2003


Autonomous Helicopter Flight via Reinforcement Learning
H. J. Kim, Michael I. Jordan, Shankar Sastry and Andrew Y. Ng
Advances in Neural Information Processing Systems 16, 2003


Statistical Debugging of Sampled Programs
Alice X. Zheng, Michael I. Jordan, Ben Liblit and Alex Aiken
Advances in Neural Information Processing Systems 16, 2003


On the Concentration of Expectation and Approximate Inference in Layered Networks
Xuanlong Nguyen and Michael I. Jordan
Advances in Neural Information Processing Systems 16, 2003


Semidefinite Relaxations for Approximate Inference on Graphs with Cycles
Michael I. Jordan and Martin J. Wainwright
Advances in Neural Information Processing Systems 16, 2003


Learning Spectral Clustering
Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 16, 2003


Kernel Dimensionality Reduction for Supervised Learning
Kenji Fukumizu, Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 16, 2003


Hierarchical Topic Models and the Nested Chinese Restaurant Process
Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum and David M. Blei
Advances in Neural Information Processing Systems 16, 2003


Latent Dirichlet Allocation
David M. Blei, Andrew Y. Ng and Michael I. Jordan
Journal of Machine Learning Research, 2003


Matching Words and Pictures
Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando D. Freitas, David M. Blei and Michael I. Jordan
Journal of Machine Learning Research, 2003


Beyond Independent Components: Trees and Clusters
Francis R. Bach and Michael I. Jordan
Journal of Machine Learning Research, 2003


A Robust Minimax Approach to Classification
Gert Lanckriet, Laurent E. Ghaoui, Chiranjib Bhattacharyya and Michael I. Jordan
Journal of Machine Learning Research, 2002


A Minimal Intervention Principle for Coordinated Movement
Emanuel Todorov and Michael I. Jordan
Advances in Neural Information Processing Systems 15, 2002


Kernel Independent Component Analysis
Francis R. Bach and Michael I. Jordan
Journal of Machine Learning Research, 2002


A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences
Eric P. Xing, Michael I. Jordan, Richard M. Karp and Stuart Russell
Advances in Neural Information Processing Systems 15, 2002


Learning Graphical Models with Mercer Kernels
Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 15, 2002


Robust Novelty Detection with Single-Class MPM
Laurent E. Ghaoui, Michael I. Jordan and Gert R. Lanckriet
Advances in Neural Information Processing Systems 15, 2002


Distance Metric Learning with Application to Clustering with Side-Information
Eric P. Xing, Michael I. Jordan, Stuart Russell and Andrew Y. Ng
Advances in Neural Information Processing Systems 15, 2002


Latent Dirichlet Allocation
David M. Blei, Andrew Y. Ng and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001


Thin Junction Trees
Francis R. Bach and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001


On Spectral Clustering: Analysis and an algorithm
Andrew Y. Ng, Michael I. Jordan and Yair Weiss
Advances in Neural Information Processing Systems 14, 2001


On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes
Andrew Y. Ng and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001


Minimax Probability Machine
Gert Lanckriet, Laurent E. Ghaoui, Chiranjib Bhattacharyya and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001


Learning with Mixtures of Trees
Marina Meila and Michael I. Jordan
Journal of Machine Learning Research, 2000