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- Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
- ICML
- 2003

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weightsâ€¦ (More)

- Zoubin Ghahramani, Michael I. Jordan
- Machine Learning
- 1995

Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through aâ€¦ (More)

- Thomas L. Griffiths, Zoubin Ghahramani
- NIPS
- 2005

We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior inâ€¦ (More)

- Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
- Machine Learning
- 1999

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number ofâ€¦ (More)

- Edward Snelson, Zoubin Ghahramani
- NIPS
- 2005

We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations ofM pseudo-input points, which we learn by a gradient based optimization. We take M N ,â€¦ (More)

Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing di erent local factor modelsâ€¦ (More)

- Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos, Zoubin Ghahramani
- Journal of Machine Learning Research
- 2010

How can we generate realistic networks? In addition, how can we do so with a mathematically tractable model that allows for rigorous analysis of network properties? Real networks exhibit a long listâ€¦ (More)

We investigate the use of unlabeled data to help labeled data in classification. We propose a simple iterative algorithm, label propagation, to propagate labels through the dataset along high densityâ€¦ (More)

- Sam T. Roweis, Zoubin Ghahramani
- Neural Computation
- 1999

Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervisedâ€¦ (More)

- David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
- NIPS
- 1994

For many types of machine learning algorithms, one can compute the statistically \optimal" way to select training data. In this paper, we review how optimal data selection techniques have been usedâ€¦ (More)