Research Papers Available

Chronological

• Ghahramani, Z., Griffiths, T.L., Sollich, P. (2007)
  Bayesian nonparametric latent feature models (with discussion and rejoinder).
  Bayesian Statistics 8. Oxford University Press.

• Wolpert, D. M. and Ghahramani, Z. (to appear)
  Bayes rule in perception, action and cognition.
  Gregory, R.L. (ed) The Oxford Companion to the Mind.

• Snelson, E., and Ghahramani, Z. (2007)
  Local and global sparse Gaussian process approximations.
  To appear in Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.

• Heller, K.A., and Ghahramani, Z. (2007)
  A Nonparametric Bayesian Approach to Modeling Overlapping Clusters.
  To appear in Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.

• Silva, R., Heller, K.A., and Ghahramani, Z. (2007)
  Analogical Reasoning with Relational Bayesian Sets.
  To appear in Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.

• Teh, Y.W., Gorur, D. and Ghahramani, Z. (2007)
  Stick-breaking Construction for the Indian Buffet.
  To appear in Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.

• Meeds, E., Ghahramani, Z., Neal, R. and Roweis, S.T. (2007)
  Modeling Dyadic Data with Binary Latent Factors.
  In Advances in Neural Information Processing Systems 19 (NIPS-2006). Cambridge, MA: MIT Press.

• Chu, W., Sindhwani, V., Keerthi, S., Ghahramani, Z. (2007)
  Relational Gaussian Processes.
  In Advances in Neural Information Processing Systems 19 (NIPS-2006). Cambridge, MA: MIT Press.

• Perez-Cruz, F., Ghahramani, Z. and Pontil, M. (2006)
  Conditional Graphical Models.
  In Bakir, F. et al. (eds) Predicting Structured Data. MIT Press.

• Zhu, X., Kandola, J., Lafferty, J. and Ghahramani, Z. (2006)
  Graph Kernels by Spectral Transforms.
  In Chapelle, O., Schoelkopf, B. and Zien, A. (eds) Semi-Supervised Learning. MIT Press.

• Podtelezhnikov, A. A., Ghahramani, Z., Wild, D.L. (2006)
  Learning about Protein Hydrogen Bonding by Minimizing Contrastive Divergence.
  Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.21247, Published online 15 Nov 2006.

• Snelson, E., and Ghahramani, Z. (2006)
  Variable noise and dimensionality reduction for sparse Gaussian processes.
  In Uncertainty in Artificial Intelligence (UAI-2006).

• Silva, R. and Ghahramani, Z. (2006)
  Bayesian Inference for Gaussian Mixed Graph Models.
  In Uncertainty in Artificial Intelligence (UAI-2006).

• Wood, F., Griffiths, T.L. and Ghahramani, Z. (2006)
  A Non-Parametric Bayesian Method for Inferring Hidden Causes.
  In Uncertainty in Artificial Intelligence (UAI-2006) pp. 536-543.

• Murray, I.A., Ghahramani, Z., and MacKay, D.J.C. (2006)
  MCMC for doubly-intractable distributions.
  In Uncertainty in Artificial Intelligence (UAI-2006).

• Azran, A. and Ghahramani, Z. (2006)
  A New Approach to Data Driven Clustering.
  In International Conference on Machine Learning (ICML-2006).

• Azran, A. and Ghahramani, Z. (2006)
  Spectral methods for automatic multiscale data clustering.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2006).

• Heller, K.A. and Ghahramani, Z. (2006)
  A Simple Bayesian Framework for Content-Based Image Retrieval.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2006).

• Beal, M.J. and Ghahramani, Z. (2006)
  Variational Bayesian learning of directed graphical models with hidden variables.
  Bayesian Analysis 1:793--832.

• Ghahramani, Z. and Heller, K.A. (2006)
  Bayesian Sets.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Snelson, E. and Ghahramani, Z. (2006)
  Sparse Gaussian Processes using Pseudo-Inputs.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Murray, I., MacKay, D.J.C., Ghahramani, Z. and Skilling, J. (2006)
  Nested Sampling for Potts Models.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Griffiths, T.L., and Ghahramani, Z. (2006)
  Infinite Latent Feature Models and the Indian Buffet Process.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Kim, H.-C., Ghahramani, Z. (2006)
  Bayesian Gaussian Process Classification with the EM-EP Algorithm.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12):1948--1959.

• Zhang, J., Ghahramani, Z. and Yang, Y. (2006)
  Learning Multiple Related Tasks using Latent Independent Component Analysis.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Snelson, E., and Ghahramani, Z. (2005)
  Compact approximations to Bayesian predictive distributions.
  In Twenty-second International Conference on Machine Learning (ICML-2005).

• Chu, W., and Ghahramani, Z. (2005)
  Preference Learning with Gaussian Processes.
  In Twenty-second International Conference on Machine Learning (ICML-2005).

• Heller, K.A. and Ghahramani, Z. (2005)
  Bayesian Hierarchical Clustering,
  Gatsby Unit Technical Report GCNU-TR 2005-002. [ps] [pdf]
  A shorter version was published in the Twenty-second International Conference on Machine Learning (ICML-2005). [pdf]

• Chu, W., and Ghahramani, Z. (2005)
  Gaussian Processes for Ordinal Regression
  Journal of Machine Learning Research, 6:1019--1041. [abstract]

• Griffiths, T. L., and Ghahramani, Z. (2005)
  Infinite latent feature models and the Indian buffet process.
  Gatsby Unit Technical Report GCNU-TR 2005-001.

• Murray, I. and Ghahramani, Z. (2005)
  A note on the evidence and Bayesian Occam’s razor.
  Gatsby Unit Technical Report GCNU-TR 2005-003. August 2005. [Abstract, PDF, DjVu, JavaDjVu].
• Chu, W., Ghahramani, Z., Falciani, F., and Wild, D. L. (2005)
  Biomarker Discovery in Microarray Gene Expression Data with Gaussian Processes.
  Bioinformatics, 21(16):3385-3393. [abstract] [official pdf file at Bioinformatics website]

• Zhu, X., Ghahramani, Z., and Lafferty, J. (2005)
  Time-Sensitive Dirichlet Process Mixture Models.
  Carnegie Mellon University Technical Report CMU-CALD-05-104.

• Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C., and Wild, D. L. (2005)
  A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.
  Bioinformatics 21(3):349-356.

• Zhu, X., Kandola, J., Ghahramani, Z. and Lafferty, J. (2005)
  Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning.
  In Advances in Neural Information Processing Systems 17. (NIPS-2004)

• Zhang, J., Ghahramani, Z. and Yang, Y. (2005)
  A Probabilistic Model for Online Document Clustering with Application to Novelty Detection [ps]. [pdf]
  In Advances in Neural Information Processing Systems 17. (NIPS-2004)

• Heller, K.A., and Ghahramani, Z. (2005)
  Randomized Algorithms for Fast Bayesian Hierarchical Clustering.
  Statistics and Optimization of Clustering Workshop, Windsor, UK.

• Chu, W. Ghahramani, Z. and Wild D.L. (2004)
  A Graphical Model for Protein Secondary Structure Prediction.
  In Proceedings of the Twenty-First International Conference on Machine Learning (ICML-2004). Morgan-Kaufmann, pp. 161-168.

• Murray, I. and Ghahramani, Z. (2004)
  Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms.
  In Uncertainty in Artificial Intelligence (UAI-2004).

• Chu, W., Ghahramani, Z. and Wild, D.L. (2004)
  A graphical model for protein secondary structure prediction [pdf]. [ps] [webserver] [slides] [poster]
  In Twenty-first International Conference on Machine Learning (ICML-04). Banff, Alberta, Canada.
• Snelson, E., Rasmussen, C.E., and Ghahramani, Z. (2004)
  Warped Gaussian Processes.
  In Advances in Neural Information Processing Systems 16. (NIPS-2003) Cambridge, MA: MIT Press.

• Qi, Y, Minka, T.P., Picard, R.W., and Ghahramani, Z. (2004)
  Predictive Automatic Relevance Determination by Expectation Propagation
  In Twenty-first International Conference on Machine Learning (ICML-04). Banff, Alberta, Canada.
• Tuttle, E. and Ghahramani, Z. (2004)
  Propagating Uncertainty in POMDP Value Iteration with Gaussian Processes [ps] [pdf]
  Gatsby Technical Report.
• Ghahramani, Z. (2004)
  Unsupervised Learning.
  In Bousquet, O., von Luxburg, U. and Raetsch, G. Advanced Lectures in Machine Learning. Lecture Notes in Computer Science 3176, pages 72-112. Berlin: Springer-Verlag.

• Minka, T.P., and Ghahramani, Z. (2003)
  Expectation Propagation for Infinite Mixtures.
  Technical Report, presented at the NIPS 2003 Workshop on Nonparametric Bayesian Methods and Infinite Models.
  Talk and abstract at this website.
• Kim, H.-C. and Ghahramani, Z. (2003)
  The EM-EP Algorithm for Gaussian Process Classification
  In the Proceedings of the Workshop on Probabilistic Graphical Models for Classification (at ECML). Dubrovnik, Croatia.
• Zhu, X., Lafferty, J., and Ghahramani, Z. (2003)
  Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes.
  CMU tech report CMU-CS-03-175 , 2003. [gzipped ps | pdf]
• Ghahramani, Z. and Kim, H.-C. (2003)
  Bayesian Classifier Combination
  Gatsby Technical Report
• Zhu, X., Lafferty, J. and Ghahramani, Z. (2003)
  Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.
  In Proc. of the ICML 2003 workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining. pp. 58-65 [gzipped ps | pdf]
• Zhu, X., Ghahramani, Z. and Lafferty, J.(2003)
  Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
  The Twentieth International Conference on Machine Learning (ICML-2003). pp 912-919[gzipped ps | pdf]
• Jin, R. and Ghahramani, Z. (2003)
  Learning with Multiple Labels.
  In Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.

• Rasmussen, C. E. and Ghahramani, Z. (2003)
  Bayesian Monte Carlo.
  In Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.

• Ghahramani, Z. (2002)
  Graphical models: parameter learning.
  In Arbib, M. A. (ed.) Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.

• Ghahramani, Z. (2002)
  Information Theory.
  In Encyclopedia of Cognitive Science. Maxmillan Reference Ltd.

• Wild, D. L., Rasmussen, C. E., and Ghahramani, Z. (2002)
  A Bayesian approach to modeling uncertainty in gene expression clusters. International Conference on Systems Biology (ICSB).

• Zhu, X., and Ghahramani, Z. (2002) Towards Semi-Supervised Classification with Markov Random Fields
  CMU CALD tech report CMU-CALD-02-106, 2002. [pdf]

• Zhu, X., and Ghahramani, Z. (2002) Learning from Labeled and Unlabeled Data with Label Propagation
  CMU CALD tech report CMU-CALD-02-107, 2002. [gzipped ps | pdf | short version]
• Beal, M. J. and Ghahramani, Z. (2002) 
  The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures [pdf] [abstract]  
  In Bayesian Statistics 7

• Ueda, N. and Ghahramani, Z. (2002)
  Bayesian model search for mixture models based on optimizing variational bounds
  Neural Networks 15: 1223-1241.

• Beal, M. J., Ghahramani, Z. and Rasmussen, C. E. (2002) 
  The Infinite Hidden Markov Model [ps.gz] [pdf] [abstract]  
  In Dietterich, T.G., Becker, S. and Ghahramani, Z. (eds) Neural Information Processing Systems 14: 577-585. Cambridge, MA, MIT Press.

• Rasmussen, C. E and Ghahramani, Z. (2002)
  Infinite Mixtures of Gaussian Process Experts [ps] [abstract]
  In Dietterich, T.G., Becker, S. and Ghahramani, Z. (eds) Neural Information Processing Systems 14: 881-888. Cambridge, MA, MIT Press.

• Korenberg, A.T. and Ghahramani, Z. (2002) A Bayesian view of motor adaptation.
  Current Psychology of Cognition 21 (4-5): 537-564.

• Ghahramani, Z. and Beal, M.J. (2001)
  Propagation algorithms for variational Bayesian learning [ps] [pdf] [abstract]
  In Leen, T.K., Dietterich, T.G., and Tresp, V. (eds) Neural Information Processing Systems 13:507-513. MIT Press.

• Rasmussen, C.E., and Ghahramani, Z (2001)
  Occam's Razor [ps] [pdf]
  In Leen, T.K., Dietterich, T.G., and Tresp, V. (eds) Neural Information Processing Systems 13:294-300. MIT Press.

• Wolpert DM, Ghahramani Z, Flanagan JR (2001)
  Perspectives and problems in motor learning
  Trends in Cognitive Science 5(11):487-494.

• Ghahramani, Z. (2001)
  An Introduction to Hidden Markov Models and Bayesian Networks [ps] [pdf]
  International Journal of Pattern Recognition and Artificial Intelligence 15(1):9-42.

• Ghahramani, Z. (2000) 
  Online Variational Bayesian Learning [pdf] [ps]  
  Slides from talk presented at NIPS 2000 workshop on Online Learning.
  Joint work with H. Attias

• Roweis, S. and Ghahramani, Z. (2000)
  An EM Algorithm for Identification of Nonlinear Dynamical Systems [ps] [pdf]
  Preprint.

• Ghahramani, Z. and Beal, M.J. (2000)
  Graphical models and variational methods
  In Saad & Opper (eds) Advanced Mean Field Method---Theory and Practice. MIT Press

• Wolpert, D.M. and Ghahramani, Z. (2000)
  Computational Principles of Movement Neuroscience .
  Nature Neuroscience 3 supp:1212--1217.

• Ghahramani, Z. (2000)
  Building blocks of movement (News & Views, on Thoroughman and Shadmehr article).
  Nature 407:682--683.

• Ghahramani, Z. and Beal, M.J. (1999)
  Variational inference for Bayesian mixtures of factor analysers [ps] [pdf] [abstract]
  In Neural Information Processing Systems 12

• Ueda, N., Nakano, R., Ghahramani, Z., and Hinton, G. E. (2000)
  SMEM Algorithm for Mixture Models. [ps] [pdf]
  In Neural Computation 12(9):2109-2128.

• Ghahramani, Z. and Roweis, S. (1999)
  Learning nonlinear dynamical systems using an EM algorithm.
  In M. S. Kearns, S. A. Solla, D. A. Cohn, (eds.) Advances in Neural Information Processing Systems 11:599-605. MIT Press.

• Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. (1999)
  An introduction to variational methods for graphical models
  Machine Learning 37:183-233.

• Roweis, S. and Ghahramani, Z. (1999)
  A Unifying Review of Linear Gaussian Models
  Neural Computation 11(2):305--345 [abstract]

• Ghahramani, Z., Korenberg, A., and Hinton, G.E. (1999)
  Scaling in a Hierarchical Unsupervised Network. In ICANN 99: Ninth international conference on Artificial Neural Networks

• Ghahramani, Z. and Hinton, G.E. (2000)
  Variational learning for switching state-space models
  Neural Computation, 12(4):963-996. [abstract]

• Ghahramani, Z. (1998)
  Learning Dynamic Bayesian Networks
  In C.L. Giles and M. Gori (eds.), Adaptive Processing of Sequences and Data Structures . Lecture Notes in Artificial Intelligence, 168-197. Berlin: Springer-Verlag. [abstract]

• Ghahramani, Z. and Hinton, G.E. (1998)
  Hierarchical Nonlinear Factor Analysis and Topographic Maps
  In Jordan, M.I, Kearns, M.J., and Solla, S.A. Advances in Neural Information Processing Systems 10. MIT Press: Cambridge, MA. [abstract]

• Ghahramani, Z. and Wolpert, D.M. (1997)
  Modular Decomposition in Visuomotor Learning
  Nature 386:392-395. [abstract]

• Ghahramani, Z., Wolpert, D.M. and Jordan, M.I. (1997)
  Computational Models of Sensorimotor Integration
  In P. G. Morasso and V. Sanguineti (eds.), Self-Organization, Computational Maps and Motor Control, Elsevier Press, pp. 117-147. [abstract] [postscript] [pdf]

• Hinton, G.E. and Ghahramani, Z. (1997)
  Generative Models for Discovering Sparse Distributed Representations [pdf] [ps.gz]
  Philosophical Transactions Royal Society B, 352:1177-1190. [abstract]
  Software written in Matlab.

• Ghahramani, Z. (1997, revised 2002)
  On Structured Variational Approximations [pdf] [ps]
  University of Toronto Technical Report CRG-TR-97-1, 6 pages (short note)

• Hinton, G.E., Sallans, B. and Ghahramani, Z. (1997)
  A Hierarchical Community of Experts [pdf] [ps.gz]
  In M.I. Jordan (ed.), Learning in Graphical Models, Kluwer Academic Publishers

• Ghahramani, Z. and Jordan, M.I. (1997)
  Factorial Hidden Markov Models
  Machine Learning 29: 245-273. [abstract]
  Software written in Matlab.

• Jordan, M.I, Ghahramani, Z. and Saul, L.K. (1997)
  Hidden Markov Decision Trees [pdf] [ps.gz]
  In Advances in Neural Information Processing Systems 9, 7 pages.

• Ghahramani, Z. and Hinton, G.E. (1996)
  The EM Algorithm for Mixtures of Factor Analyzers [pdf] [ps]
  University of Toronto Technical Report CRG-TR-96-1, 8 pages (short note).
  Software written in Matlab.

• Ghahramani, Z. and Hinton, G.E. (1996)
  Parameter estimation for linear dynamical systems [pdf] [ps]
  University of Toronto Technical Report CRG-TR-96-2, 6 pages (short note).

• Cohn, D.A., Ghahramani, Z., and Jordan, M.I. (1996)
  Active Learning with Statistical Models [pdf] [ps]
  Journal of Artificial Intelligence Research 4: 129-145. [abstract]

• Ghahramani, Z., Wolpert, D.M. and Jordan, M.I. (1996)
  Generalization to Local Remappings of the Visuomotor Coordinate Transformation
  Journal of Neuroscience 16:7085-7096. [abstract] [pdf]

• Wolpert, D.M., Ghahramani, Z. and Jordan, M.I. (1995)
  An Internal Model for Sensorimotor Integration
  Science 269: 1880-1882. [abstract]

• Ghahramani, Z. (1995)
  Factorial Learning and the EM Algorithm [pdf] [ps]
  In G. Tesauro, D.S. Touretzky, and J. Alspector (eds.), Advances in Neural Information Processing Systems 7, 8 pages. [abstract]

• Ghahramani, Z., Wolpert, D.M. and Jordan, M.I. (1995)
  Computational structure of coordinate transformations: A generalization study
  In G. Tesauro, D.S. Touretzky, and J. Alspector (eds.), Advances in Neural Information Processing Systems 7 [8 pages] [abstract]

• Ghahramani, Z. (1995)
  Computation and Psychophysics of Sensorimotor Integration [ps.gz]
  Ph.D. Thesis, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 171 pages, 1 MB. [abstract]

• Ghahramani, Z. and Jordan, M.I. (1994)
  Learning from incomplete data [pdf] [ps.gz]
  MIT Center for Biological and Computational Learning Technical Report 108, 16 pages. [abstract]

• Ghahramani, Z. and Jordan, M.I. (1994)
  Supervised learning from incomplete data via an EM approach [ps] [pdf]
  In Cowan, J.D., Tesauro, G., and Alspector, J. (eds.). Advances in Neural Information Processing Systems 6, 8 pages. [abstract]

• Ghahramani, Z. (1994)
  Solving inverse problems using an EM approach to density estimation [pdf][ps.gz]
  In M.C. Mozer, P. Smolensky, D.S. Touretzky, J.L. Elman, & A.S. Weigend (eds.), Proceedings of the 1993 Connectionist Models Summer School, 8 pages. [abstract]