- A HIERARCHICAL COMMUNITY OF EXPERTS GEOFFREY E. HINTON
- SMEM Algorithm for Mixture Models Naonori Ueda Ryohei Nakano
- A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- Statistical Approaches to Learning and Discovery The EM Algorithm
- Encyclopedia of Cognitive Science--Author Stylesheet Copyright Macmillan Reference Ltd 16 February, 2007 Page 1
- On Structured Variational Approximations Zoubin Ghahramani
- Machine Learning, ?, 1--31 (1997) fl 1997 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Statistical Approaches to Learning and Discovery Reinforcement Learning
- Draft version; accepted for NIPS*03 Warped Gaussian Processes
- Computational structure of coordinate transformations: A generalization study
- Image Searching and Modelling Part IB Paper 8
- Bayesian model search for mixture models based on optimizing variational bounds
- A HIERARCHICAL COMMUNITY OF EXPERTS GEOFFREY E. HINTON
- To appear: M. I. Jordan, (Ed.), Learning in Graphical Models, Kluwer Academic Publishers.
- ;~~ Cahiers de PsychologieCognitive/ ~ Current Psychology of Cognition
- Bayesian Inference for Gaussian Mixed Graph Models Ricardo Silva
- Online Variational Bayesian Learning Zoubin Ghahramani
- 4F13: Machine Learning Lecture 3-4: Unsupervised Learning
- BIOINFORMATICS ORIGINAL PAPER Vol. 21 no. 3 2005, pages 349356
- Statistical Approaches to Learning and Discovery Graphical Models
- Lecture Outline 1. EM Algorithm for MLE (maximum likelihood estimation)
- Gibbs sampling (an MCMC method) and relations to EM Lecture Outline
- Journal of Artificial Intelligence Research 4 (1996) 129145 Submitted 11/95; published 3/96 Active Learning with Statistical Models
- Bayes rule in perception, action and cognition Daniel M. Wolpert and Zoubin Ghahramani
- Factorial Learning and the EM Algorithm Zoubin Ghahramani
- Learning with Multiple Labels Rong Jin* Zoubin Ghahramani*
- Expectation propagation for infinite mixtures (Extended abstract)
- A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
- Roles for Statistical Models Data Reduction and factorization of the likelihood function.
- letters to nature 742 NATURE |VOL 407 |12 OCTOBER 2000 |www.nature.com
- Online Variational Bayesian Learning Zoubin Ghahramani
- Journal of Arti cial Intelligence Research 4 (1996) 129-145 Submitted 11/95; published 3/96 Active Learning with Statistical Models
- Automatic Causal Discovery Richard Scheines
- Proc. Valencia / ISBA 8th World Meeting on Bayesian Statistics Benidorm (Alicante, Spain), June 1st6th, 2006
- Rejoinder for "Bayesian Nonparametric Latent Feature Models"
- Sparse Gaussian Processes using Pseudo-inputs Edward Snelson Zoubin Ghahramani
- Bayesian Gaussian Process Classification with the EM-EP Algorithm
- Gatsby Computational Neuroscience Unit 17 Queen Square, London University College London WC1N 3AR, United Kingdom
- A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
- Randomized Algorithms for Fast Bayesian Hierarchical Clustering Katherine A. Heller and Zoubin Ghahramani
- A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk
- A Graphical Model for Protein Secondary Structure Prediction
- The EM-EP Algorithm for Gaussian Process Classification
- Graphical models: parameter learning Zoubin Ghahramani
- Scaling in a Hierarchical Unsupervised Network1 Zoubin Ghahramani,2
- Learning Dynamic Bayesian Networks? Zoubin Ghahramani
- Toappear in Jordan, MI, Kearns MJ, and Solla,SA Advances in Neural Information Processing Systems 10. MIT Press: Cambridge, MA, 1998.
- Modular decomposition in visuomotor learning Zoubin Ghahramani y
- Machine Learning, ?, 1{31 (1997) c 1997 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Parameter Estimation for Linear Dynamical Systems Zoubin Ghahramani
- The Variational Kalman Smoother GCNU TR 2001-003
- 4F13: Machine Learning Lecture 5: Unsupervised Learning: ICA and EM
- 4F13: Machine Learning Propagation on Factor Graphs
- 4F13: Machine Learning Lecture 10: Variational Approximations
- 4F13: Machine Learning Lecture 11: Bayesian Model Comparison
- 4F13: Machine Learning Lectures 12-14: Reinforcement Learning
- BIOINFORMATICS Vol. 20 no. 9 2004, pages 13611372
- Information Engineering Option (paper 8) Photo Editing and Image Search
- Image Searching and Modelling Part IB Paper 8
- Some Asymptotic Bayesian Inference (background to Chapter 2 of Tanner's book)
- Statistical Approaches to Learning and Discovery Latent Variable Models
- Gibbs sampling (an MCMC method) and relations to EM Lectures Outline
- Statistical Approaches to Learning and Discovery Variational Approximations
- Statistical Approaches to Learning and Discovery Latent Variable Time Series Models
- Statistical Approaches to Learning and Discovery Bayesian Model Selection
- MACHINE LEARNING SAMPLE EXAM PAPER 4F13 Michaelmas, 2006
- in Advances in Neural Information Processing Systems 14, MIT Press (2002). Infinite Mixtures of Gaussian Process Experts
- Solving inverse problems using an EM approach to density estimation
- Hidden Markov decision trees \Lambda Michael I. Jordan y , Zoubin Ghahramani z , and Lawrence K. Saul y
- Hidden Markov decision trees Michael I. Jordany, Zoubin Ghahramaniz, and Lawrence K. Sauly
- To appear in M. S. Kearns, S. A. Solla, D. A. Cohn, (eds.) Advances in Neural Information Processing Systems 11. Cambridge, MA: MIT Press, 1999.
- Computational structure of coordinate transformations: A generalization study
- Generative Models for Discovering Sparse Distributed Representations
- Bayesian Hierarchical Clustering Katherine A. Heller heller@gatsby.ucl.ac.uk
- Modular decomposition in visuomotor learning Zoubin Ghahramani \Lambday and Daniel M. Wolpert z
- PAPER 8 Image Processing -2007 Sample Exam Question Below is a 5-part question. The actual exam question will have 3 parts.
- Lecture Outline (1) Maximum Likelihood and Normal Inference
- 4F13: Machine Learning Lectures 1-2: Introduction to Machine Learning
- Journal of Machine Learning Research 6 (2005) 10191041 Submitted 11/04; Revised 3/05; Published 7/05 Gaussian Processes for Ordinal Regression
- Remarks on Improper "Ignorance" Priors As a limit of proper priors
- Learning from Labeled and Unlabeled Data with Label Propagation
- Generative Models for Discovering Sparse Distributed Representations
- Gatsby Technical Report: Propagating Uncertainty in POMDP Value
- Computation and Psychophysics of Sensorimotor Integration
- BIOINFORMATICS Biomarker Discovery in Microarray Gene
- Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
- Time-Sensitive Dirichlet Process Mixture Models Xiaojin Zhu Zoubin Ghahramani John Lafferty
- Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions Xiaojin Zhu
- The EM Algorithm for Mixtures of Factor Analyzers Zoubin Ghahramani
- Supervised learning from incomplete data via an EM approach
- Spectral Methods for Automatic Multiscale Data Clustering Gatsby Computational Neuroscience Unit
- Occam's Razor Carl Edward Rasmussen
- Parameter Estimation for Linear Dynamical Systems Zoubin Ghahramani
- Generalization to Local Remappings of the Visuomotor Coordinate Transformation
- Bayesian Hierarchical Clustering Katherine A. Heller heller@gatsby.ucl.ac.uk
- Discussion of "Bayesian Nonparametric Latent Feature Models" by Zoubin Ghahramani
- Image Searching and Modelling Part IB Paper 8
- Towards Semi-Supervised Classi cation with Markov Random Fields
- Computation and Psychophysics of Sensorimotor Integration
- DRAFT. TO APPEAR IN SIMON HAYKIN ED., KALMAN FILTERING AND NEURAL NETWORKS 1 An EM Algorithm for Identi cation of
- 4F13: Machine Learning Lectures 6-7: Graphical Models
- Sequential Decisions A Basic Theorem of (Bayesian) Expected Utility Theory
- Statistical Approaches to Learning and Discovery Lecture 1: Introduction, Statistical Basics, and a bit of Information Theory
- The EM Algorithm for Mixtures of Factor Analyzers Zoubin Ghahramani
- Probabilistic Models for Unsupervised Learning
- Solving inverse problems using an EM approach to density estimation
- Predictive Automatic Relevance Determination by Expectation Propagation
- Factorial Learning and the EM Algorithm Zoubin Ghahramani
- Preference Learning with Gaussian Processes Wei Chu chuwei@gatsby.ucl.ac.uk
- Bayesian Analysis (2006) 1, Number 4, pp. 793832 Variational Bayesian Learning of Directed
- A Unifying Review of Linear Gaussian Models Sam Roweis Zoubin Ghahramani
- An Introduction to Hidden Markov Models and Bayesian Networks Zoubin Ghahramani
- A New Approach to Data Driven Clustering Arik Azran arik@gatsby.ucl.ac.uk
- A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk
- Statistical Approaches to Learning and Discovery Markov Chain Monte Carlo
- Learning from Labeled and Unlabeled Data with Label Propagation
- Graphical models and variational methods Zoubin Ghahramani and Matthew J. Beal
- Bayesian Classifier Combination Zoubin Ghahramani and Hyun-Chul Kim
- A Simple Bayesian Framework for Content-Based Image Retrieval Katherine A. Heller
- 4F13: Machine Learning A Belief Propagation Demo
- Stick-breaking Construction for the Indian Buffet Process Yee Whye Teh
- Computational Models of Sensorimotor Integration Zoubin Ghahramani \Lambda
- 1212 nature neuroscience supplement volume 3 november 2000 The computational study of motor control is fundamentally con-
- Bayesian Segmental Models with Multiple Sequence Alignment Profiles
- Unsupervised Learning Zoubin Ghahramani
- To appear in M. S. Kearns, S. A. Solla, D. A. Cohn, (eds.) Advances in Neural Information Processing Systems 11. Cambridge, MA: MIT Press, 1999.
- Analogical Reasoning with Relational Bayesian Sets Ricardo Silva
- A Non-Parametric Bayesian Method for Inferring Hidden Causes Computer Science
- The Variational Kalman Smoother GCNU TR 200103
- Variational Learning for Switching State-Space Models Zoubin Ghahramani
- Probabilistic Models for Unsupervised Learning
- A Bayesian Approach to Modeling Uncertainty in Gene Expression Clusters
- 1 Graph Kernels by Spectral Transforms Xiaojin Zhu
- Learning Multiple Related Tasks using Latent Independent Component Analysis
