2015 - Fellow of the Royal Society, United Kingdom
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Pattern recognition and Bayesian probability. Artificial intelligence and Gaussian process are frequently intertwined in his study. His research in the fields of Active learning overlaps with other disciplines such as Local regression.
His biological study spans a wide range of topics, including Data mining, Markov chain Monte Carlo, Covariance, Dimensionality reduction and Mixture model. Many of his research projects under Pattern recognition are closely connected to Small magnitude with Small magnitude, tying the diverse disciplines of science together. His study on Bayesian inference is often connected to Simple as part of broader study in Bayesian probability.
Artificial intelligence, Machine learning, Algorithm, Bayesian probability and Inference are his primary areas of study. His Artificial intelligence research includes elements of Gaussian process and Pattern recognition. In Machine learning, Zoubin Ghahramani works on issues like Variable-order Bayesian network, which are connected to Bayesian statistics.
His Algorithm study incorporates themes from Kernel, Markov chain Monte Carlo, Graphical model, Hidden semi-Markov model and Gibbs sampling. His research in Bayesian probability focuses on subjects like Data mining, which are connected to Cluster analysis. The Inference study combines topics in areas such as Nonparametric statistics, Theoretical computer science, Deep learning, Mathematical optimization and Hidden Markov model.
Zoubin Ghahramani focuses on Artificial intelligence, Machine learning, Inference, Bayesian probability and Estimator. His research links Gaussian process with Artificial intelligence. Much of his study explores Machine learning relationship to Adversarial system.
Zoubin Ghahramani has included themes like Markov chain Monte Carlo, Anomaly detection, Bayesian inference, Automatic differentiation and Probabilistic logic in his Inference study. His studies in Bayesian probability integrate themes in fields like Latent variable, Approximate inference, Data mining and Dropout. His Estimator study also includes fields such as
Zoubin Ghahramani spends much of his time researching Artificial intelligence, Machine learning, Bayesian probability, Gaussian process and Inference. His Artificial intelligence research includes themes of Stability and Missing data. The study incorporates disciplines such as Adversarial system and Count data in addition to Machine learning.
His Bayesian probability research is multidisciplinary, incorporating elements of Exploratory data analysis, Data mining, Directed acyclic graph and Feed forward. His Gaussian process research incorporates elements of Training set, Kernel, Key, Algorithm and Random function. His Inference study combines topics in areas such as Domain, Anomaly detection, Density estimation and Statistical data type.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Semi-supervised learning using Gaussian fields and harmonic functions
Xiaojin Zhu;Zoubin Ghahramani;John Lafferty.
international conference on machine learning (2003)
An Internal Model for Sensorimotor Integration
Daniel M. Wolpert;Zoubin Ghahramani;Michael I. Jordan.
An introduction to variational methods for graphical models
Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul.
Machine Learning (1999)
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
Yarin Gal;Zoubin Ghahramani.
international conference on machine learning (2016)
Computational principles of movement neuroscience
Daniel M. Wolpert;Zoubin Ghahramani.
Nature Neuroscience (2000)
Active learning with statistical models
David A. Cohn;Zoubin Ghahramani;Michael I. Jordan.
Journal of Artificial Intelligence Research (1996)
Factorial Hidden Markov Models
Zoubin Ghahramani;Michael I. Jordan.
neural information processing systems (1995)
Sparse Gaussian Processes using Pseudo-inputs
Edward Snelson;Zoubin Ghahramani.
neural information processing systems (2005)
Learning from labeled and unlabeled data with label propagation
X Zhu;Z Ghahramani.
Center for Automated Learning and Discovery, CMU: Carnegie Mellon University, USA. (2002)
A unifying review of linear Gaussian models
Sam Roweis;Zoubin Ghahramani.
Neural Computation (1999)
Profile was last updated on December 6th, 2021.
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