# Zoubin Ghahramani

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- Publications
- Influence

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

- Xiaojin Zhu, Zoubin Ghahramani, J. Lafferty
- Computer Science
- ICML
- 21 August 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 weights… Expand

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

- Yarin Gal, Zoubin Ghahramani
- Mathematics, Computer Science
- ICML
- 6 June 2015

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models… Expand

Infinite latent feature models and the Indian buffet process

- T. Griffiths, Zoubin Ghahramani
- Computer Science
- NIPS
- 5 December 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 in… Expand

Factorial Hidden Markov Models

- Zoubin Ghahramani, Michael I. Jordan
- Computer Science
- Machine Learning
- 27 November 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 a… Expand

Sparse Gaussian Processes using Pseudo-inputs

- Edward Snelson, Zoubin Ghahramani
- Computer Science
- NIPS
- 5 December 2005

We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M ≪… Expand

An Introduction to Variational Methods for Graphical Models

- Michael I. Jordan, Zoubin Ghahramani, T. Jaakkola, L. Saul
- Computer Science, Mathematics
- Machine Learning
- 1 February 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 of… Expand

Learning from labeled and unlabeled data with label propagation

- Xiaojin Zhu, Zoubin Ghahramani
- Computer Science
- 16 September 2002

We investigate the use of unlabeled data to help labeled data in cl ssification. We propose a simple iterative algorithm, label pro pagation, to propagate labels through the dataset along high… Expand

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An Introduction to Variational Methods for Graphical Models

- Michael I. Jordan, Zoubin Ghahramani, T. Jaakkola, L. Saul
- Computer Science
- Learning in Graphical Models
- 1998

Kronecker Graphs: An Approach to Modeling Networks

- J. Leskovec, Deepayan Chakrabarti, J. Kleinberg, C. Faloutsos, Zoubin Ghahramani
- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 29 December 2008

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 list… Expand

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

- Yarin Gal, Zoubin Ghahramani
- Computer Science, Mathematics
- NIPS
- 16 December 2015

Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail… Expand