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Yee Whye Teh
Researcher at University of Oxford
Publications - 351
Citations - 42930
Yee Whye Teh is an academic researcher from University of Oxford. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 68, co-authored 326 publications receiving 36155 citations. Previous affiliations of Yee Whye Teh include University of Toronto & University College London.
Papers
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Proceedings Article
Collapsed variational Dirichlet process mixture models
TL;DR: A number of variational Bayesian approximations to the Dirichlet process (DP) mixture model are studied and a novel collapsed VB approximation where mixture weights are marginalized out is considered.
Proceedings Article
Automatic Alignment of Local Representations
Yee Whye Teh,Sam T. Roweis +1 more
TL;DR: An automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate system for the original data space is presented.
Journal ArticleDOI
Consistency and fluctuations for stochastic gradient Langevin dynamics
TL;DR: In this article, the authors provide a rigorous mathematical framework for analysing the SGLD algorithm and show that the algorithm is consistent, satisfies a central limit theorem (CLT), and its asymptotic bias-variance decomposition can be characterized by an explicit functional of the step-sizes sequence (δm)m≥0.
Journal ArticleDOI
Energy-based models for sparse overcomplete representations
TL;DR: A new way of extending independent components analysis (ICA) to overcomplete representations that defines features as deterministic (linear) functions of the inputs and assigns energies to the features through the Boltzmann distribution.
Proceedings Article
Mondrian Forests: Efficient Online Random Forests
TL;DR: Roy and Teh as discussed by the authors used Mondrian processes to construct ensembles of random decision trees called Mondrian forests, which can be grown in an incremental/online fashion and remarkably, the distribution of online Mondrian forest is the same as that of batch Mondrian tree.