- Efficient Stepwise Selection in Decomposable Models Amol Deshpande
- Attractor dynamics in feedforward neural networks
- Learning with mixtures of trees Marina Meila
- Approximate inference algorithms for twolayer Bayesian networks
- Feature Space Resampling for Protein Conformational Search Short title: Protein Feature Space Resampling
- Published in: Neural Computation, 6, 181214, 1994. Hierarchical mixtures of experts and
- Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization
- Journal of Machine Learning Research 3 (2002) 1-48 Submitted 11/01; Published 07/02 Kernel Independent Component Analysis
- A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction
- Fast Approximate Spectral Clustering Donghui Yan Ling Huang Michael I. Jordan
- Decentralized Detection and Classification using Kernel Methods XuanLong Nguyen XUANLONG@CS.BERKELEY.EDU
- Sharing Features among Dynamical Systems with Beta Processes
- Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations
- From Advances in Neural Information Processing Systems [NIPS] 8.
- Public Deployment of Cooperative Bug Isolation # Ben Liblit +
- VC dimension A measure of the complexity of a model. Knowledge of the VC dimension per mits an estimate to be made of the difference between performance on the training set and
- On semide nite relaxation for normalized k-cut and connections to spectral clustering
- Decentralized Detection and Classification using Kernel Methods
- Nonparametric estimation of the likelihood ratio and divergence functionals
- Fast Approximate Spectral Clustering Donghui Yan
- Optimization of Structured Mean Field Objectives Alexandre Bouchard-C^ote
- Hierarchical Dirichlet Processes Yee Whye Teh
- Detecting Large-Scale System Problems by Mining Console Logs Wei Xu xuw@cs.berkeley.edu
- Managing Data Transfers in Computer Clusters with Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica
- A Statistical Approach to Decision Tree Modeling Michael I. Jordan
- Loopy Belief Propagation and Gibbs Measures Sekhar C. Tatikonda
- Learning Semantic Correspondences with Less Supervision Percy Liang
- Detecting Large-Scale System Problems by Mining Console Logs Armando Fox
- Type-Based MCMC Percy Liang
- The Annals of Statistics 2009, Vol. 37, No. 2, 876904
- IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 11, NOVEMBER 2010 5847 Estimating Divergence Functionals and the
- IEEE SIGNAL PROCESSING MAGAZINE SPECIAL ISSUE 1 Bayesian Nonparametric Methods for Learning
- JOURNAL OF COMPUTATIONAL BIOLOGY Volume 14, Number 3, 2007
- Log-determinant relaxation for approximate inference in discrete Markov random fields
- In Proceedings of the 24th International Conference on Research and Development in Information Retrieval (SIGIR), New York, NY: ACM Press, 2001. Stable Algorithms for Link Analysis
- Convergence results for the EM approach to mixtures of experts architectures \Lambda
- Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study
- Graphical Models Michael I. Jordan
- Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
- Haykin, Principe, Sejnowski, and McWhirter: New Directions in Statistical Signal Processing: From Systems to Brain 2005/03/04 21:55 11 A Variational Principle for Graphical Models
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- DRAFT Failure Diagnosis Using Decision Trees Mike Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric Brewer
- Sensorimotor Adaptation of Speech I: Compensation and Adaptation
- Hierarchical Bayesian Nonparametric Models with Applications
- The Infinite PCFG using Hierarchical Dirichlet Processes Percy Liang Slav Petrov Michael I. Jordan Dan Klein
- To appear: M. I. Jordan, (Ed.), Learning in Graphical Models, Kluwer Academic Publishers.
- LOGOS: a modular Bayesian model for de novo motif detection Eric P. Xing
- Latent Dirichlet Allocation David M. Blei, Andrew Y. Ng and Michael I. Jordan
- Semi-supervised Learning via Gaussian Neil D. Lawrence
- California, http://www.cs.berkeley.edu/ jordan
- Structured Prediction via the Extragradient Computer Science
- A generalized mean eld algorithm for variational inference in exponential families
- Journal of Machine Learning Research 5 (2004) 27-72 Submitted 10/02; Revised 8/03; Published 1/04 Learning the Kernel Matrix with Semidefinite Programming
- Generalization to local remappings of the visuomotor coordinate transformation
- Graphical models, exponential families, and variational Martin J. Wainwright
- BAYESIAN STATISTICS 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid,
- Probabilistic Inference in Queueing Networks Charles Sutton
- Smoothness Maximization Along a Predefined Path Accurately Predicts the Speed Profiles of Complex Arm Movements
- Regression on Manifolds Using Kernel Dimension Reduction Jens Nilsson JENSN@MATHS.LTH.SE
- 8 Gaussian Processes and the Null-Category Noise Model
- Machine Learning, ??, 1--11 (1998) fl 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- In H. Heuer & S. Keele, (Eds.), Handbook of Perception and Action: Motor Skills. New York: Academic Press, 1996.
- Spectral Clustering with Perturbed Data Intel Research
- Variational inference for Dirichlet process mixtures
- Cognitive Science, 16, 307354, 1992. Forward models: Supervised learning
- An Analysis of the Convergence of Graph Laplacians Daniel Ting dting@stat.berkeley.edu
- ftp://psyche.mit.edu/pub/jordan/uai.ps Why the logistic function? A tutorial discussion
- Discussion of Boosting Papers Peter L. Bartlett
- Semidefinite relaxations for approximate inference on graphs with cycles
- DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
- Mixed Membership Matrix Factorization Lester Mackey lmackey@cs.berkeley.edu
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- To appear in: M. Gazzaniga, (Ed.). The Cognitive Neurosciences. Cambridge, MA: MIT Press. Computational motor control
- Triangulation by Continuous Embedding Marina Meila and Michael I. Jordan
- Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters
- Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
- Learning From Measurements in Exponential Families Percy Liang pliang@cs.berkeley.edu
- Heavy-Tailed Process Priors for Selective Shrinkage Fabian L. Wauthier
- Kalman Filtering with Intermittent Observations
- Graph partition strategies for generalized mean field inference Eric P. Xing
- Kalman Filtering with Intermittent Observations* Bruno Sinopoli, Luca Schenato, Massimo Franceschetti,
- Learning Dependency-Based Compositional Semantics Percy Liang
- Unsupervised Kernel Dimension Reduction Meihong Wang
- Association Mapping and Significance Estimation via the Gad Kimmel1,2
- A variational principle for modelbased interpolation
- A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences
- Jordan and Weiss: Graphical Models: Probabilistic inference 1 Graphical models: Probabilistic inference
- Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection
- Perceptual distortion contributes to the curvature of human reaching movements.
- Convexity, Classification, and Risk Bounds Peter L. Bartlett #
- Loopy Belief Propagation for Approximate Inference: An Empirical Study
- BIOINFORMATICS Vol. 20 no. 16 2004, pages 26262635
- Journal of Bioinformatics and Computational Biology Vol. 2, No. 1 (2004) 127154
- Agreement-Based Learning Percy Liang
- Semiparametric Latent Factor Models Yee Whye Teh
- Modeling Annotated Data David M. Blei
- Optimal feedback control as a theory of motor coordination: Supplementary Notes
- Structured Prediction via the Extragradient Computer Science
- On divergences, surrogate loss functions, and decentralized XuanLong Nguyen
- Mixture Representations for Inference and Learning in Boltzmann Machines
- Bug Isolation via Remote Program Sampling # Ben Liblit +
- Robust Novelty Detection with Single-Class MPM
- Supplementary Material : Variational Inference over Combinatorial Spaces
- BAYESIAN STATISTICS 7, pp. 2543 J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid,
- Learning Programs: A Hierarchical Bayesian Approach Percy Liang pliang@cs.berkeley.edu
- On Spectral Clustering: Analysis and an algorithm
- Appeared in: Allerton Conference on Control, Communication and Computing
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- In press: M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press.
- Support Vector Machines for Analog Circuit Performance Representation
- A permutation-augmented sampler for DP mixture models Percy Liang pliang@cs.berkeley.edu
- Stat Comput (2010) 20: 231252 DOI 10.1007/s11222-008-9111-x
- Hierarchical Beta Processes and the Indian Buffet Process Romain Thibaux
- Autonomous helicopter flight via reinforcement learning
- Submitted to the Annals of Statistics SUPPORT UNION RECOVERY IN HIGH-DIMENSIONAL
- A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis
- Nonparametric Latent Feature Models for Link Prediction
- Hidden Markov decision trees Michael I. Jordan \Lambda , Zoubin Ghahramani y , and Lawrence K. Saul \Lambda
- Neural Computation, 8, 129--151, 1996. On Convergence Properties of the EM Algorithm for
- The Annals of Applied Statistics 2010, Vol. 4, No. 4, 16421643
- CONVEX AND SEMI-NONNEGATIVE MATRIX FACTORIZATIONS: DING, LI AND JORDAN 1 Convex and Semi-Nonnegative Matrix
- Probabilistic Grammars and Hierarchical Dirichlet Processes Percy Liang
- Protein Molecular Function Prediction by Bayesian Phylogenomics
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 11, NOVEMBER 2005 4053 Nonparametric Decentralized Detection
- Bayesian Haplotype Inference via the Dirichlet Process epxing@cs.berkeley.edu
- Efficient Inference in Phylogenetic InDel Trees Alexandre Bouchard-C^ote
- Random Conic Pursuit for Semidefinite Programming: Supplementary Materials
- Statistical Debugging of Sampled Programs Alice X. Zheng
- Coherence Functions for Multicategory Margin-based Classification Methods
- A Robust Minimax Approach to Classification Gert R.G. Lanckriet gert@eecs.berkeley.edu
- Learning in Boltzmann Trees Lawrence Saul and Michael Jordan
- Nonparametric Combinatorial Sequence Models Fabian L. Wauthier, Michael I. Jordan, and Nebojsa Jojic
- A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection
- Variational Inference over Combinatorial Spaces Alexandre Bouchard-C^ote
- Random Conic Pursuit for Semidefinite Programming Ariel Kleiner
- Hierarchical Models, Nested Models and Completely Random Measures
- Bayesian Nonparametric Learning: Expres-sive Priors for Intelligent Systems
- Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a
- Matrix-Variate Dirichlet Process Mixture Models Zhihua Zhang Guang Dai Michael I. Jordan
- Inference and Learning in Networks of Queues Charles Sutton Michael I. Jordan
- Sufficient Dimension Reduction for Visual Sequence Classification UC Berkeley
- Vol. 00 no. 00 2009 Joint estimation of gene conversion rates and mean
- Automatic Exploration of Datacenter Performance Regimes Peter Bodk, Rean Griffith, Charles Sutton,
- Latent Variable Models for Dimensionality Reduction Zhihua Zhang
- Online System Problem Detection by Mining Patterns of Console Logs Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael Jordan
- High-dimensional support union recovery in multivariate regression
- Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
- Spectral Clustering with Perturbed Data Intel Research
- Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
- On the Inference of Ancestries in Admixed Populations Sriram Sankararaman,1
- A Dual Receptor Cross-talk Model of G protein-coupled Signal Transduction Running Head: Dual Receptor GPCR Crosstalk Model
- The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
- An HDP-HMM for Systems with State Persistence Emily B. Fox ebfox@mit.edu
- An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators
- IMAGE DENOISING WITH NONPARAMETRIC HIDDEN MARKOV TREES Jyri J. Kivinen
- Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
- Journal of Machine Learning Research 7 (2006) 1963-2001 Submitted 3/05; Revised 7/06; Published 10/06 Learning Spectral Clustering, With Application To Speech Separation
- BioMed Central Page 1 of 19
- Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture
- Bayesian Multicategory Support Vector Machines Zhihua Zhang
- On optimal quantization rules for sequential decision problems
- Robust design of biological experiments Patrick Flaherty
- Divergences, surrogate loss functions and experimental design
- Subtree power analysis and species selection for comparative genomics
- A kernel-based learning approach to ad hoc sensor network localization
- A Probabilistic Interpretation of Canonical Correlation Analysis
- Extensions of the Informative Vector Machine Neil D. Lawrence1
- MULTI-INSTRUMENT MUSICAL TRANSCRIPTION USING A DYNAMIC GRAPHICAL Brian K. Vogela,c
- On Information Divergence Measures, Surrogate Loss Functions and Decentralized Hypothesis Testing
- A direct formulation for sparse PCA using semidefinite programming
- Variational methods for the Dirichlet process David M. Blei blei@cs.berkeley.edu
- KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST
- Combining Statistical Monitoring and Predictable Recovery for Self-Management
- Large margin classifiers: convex loss, low noise, and convergence rates
- On the concentration of expectation and approximate inference in layered networks
- Learning from Dyadic Data To appear in
- THE STICKY HDP-HMM: BAYESIAN NONPARAMETRIC HIDDEN MARKOV MODELS WITH PERSISTENT STATES
- Sparse Gaussian Process Classification With Multiple Classes Matthias Seeger
- Dimensionality Reduction for Spectral Clustering Donglin Niu Jennifer G. Dy Michael I. Jordan
- Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
- Bayesian Generalized Kernel Models Zhihua Zhang Guang Dai Donghui Wang Michael I. Jordan
- The DLR Hierarchy of Approximate Inference Michal Rosen-Zvi
- Distance metric learning, with application to clustering with sideinformation
- Kernel Dimensionality Reduction for Supervised Kenji Fukumizu
- Journal of Machine Learning Research 3 (2003) 9931022 Submitted 2/02; Published 1/03 Latent Dirichlet Allocation
- A Minimal Intervention Principle for Coordinated Movement
- Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes Jyri J. Kivinen
- Response-Time Modeling for Resource Allocation and Energy-Informed SLAs
- Feature Selection for HighDimensional Genomic Microarray Data Eric P. Xing + epxing@cs.berkeley.edu
- PEGASUS: A policy search method for large MDPs and POMDPs Andrew Y. Ng
- Bayesian parameter estimation via variational methods Tommi S. Jaakkola Michael I. Jordan
- Link Analysis, Eigenvectors and Stability Andrew Y. Ng
- Appendix for High-dimensional union support recovery in multivariate regression
- Exploiting Tractable Substructures in Intractable Networks
- Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies
- Spectral clustering for speech separation Francis R. Bach francis.bach@mines.org
- Journal of Machine Learning Research 7 (2006) 16271653 Submitted 10/05; Published 7/06 Structured Prediction, Dual Extragradient and Bregman Projections
- Statistical Science 2008, Vol. 23, No. 3, 383403
- Word Alignment via Quadratic Assignment Simon Lacoste-Julien
- 2003/09/30 13:09 1 Kernel-based Integration of Genomic Data
- Boltzmann Chains and Hidden Markov Models
- Modeling Events with Cascades of Poisson Processes Aleksandr Simma
- Journal of Machine Learning Research 11 (2010) 2199-2228 Submitted 11/09; Revised 5/10; Published 8/10 Regularized Discriminant Analysis, Ridge Regression and Beyond
- On optimal quantization rules for some problems in sequential decentralized detection
- The Annals of Applied Statistics 2011, Vol. 5, No. 2A, 10201056
- Version dated: September 23, 2011 PHYLOGENETIC INFERENCE VIA SEQUENTIAL MONTE CARLO
- Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier
- Divide-and-Conquer Matrix Factorization Lester Mackeya
- Visually Relating Gene Expression and in vivo DNA Binding Data Min-Yu Huang, Lester Mackey, Soile V. E. Keranen, Gunther H. Weber, Michael I. Jordan,
- Stick-Breaking Beta Processes and the Poisson Process John Paisley1
