Yee Whye Teh

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Yee-Whye Teh
Alma mater
ThesisBethe free energy and contrastive divergence approximations for undirected graphical models (2003)
Doctoral advisorGeoffrey Hinton[3]
Websitewww.stats.ox.ac.uk/~teh/ Edit this at Wikidata

Yee-Whye Teh is a professor of

Department of Statistics, University of Oxford.[4][5] Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London.[6] His work is primarily in machine learning, artificial intelligence, statistics and computer science.[1][7]

Education

Teh was educated at the

PhD in 2003 for research supervised by Geoffrey Hinton.[3][8]

Research and career

Teh was a

Teh was one of the original developers of deep belief networks[9] and of hierarchical Dirichlet processes.[10]

Awards and honours

Teh was a keynote speaker at Uncertainty in Artificial Intelligence (UAI) 2019, and was invited to give the Breiman lecture at the Conference on Neural Information Processing Systems (NeurIPS) 2017.[11] He served as program co-chair of the International Conference on Machine Learning (ICML) in 2017, one of the premier conferences in machine learning.[4]

References

  1. ^ a b Yee Whye Teh publications indexed by Google Scholar Edit this at Wikidata
  2. ^ a b "Yee-Whye Teh, Professor of Statistical Machine Learning". stats.ox.ac.uk.
  3. ^ a b Yee Whye Teh at the Mathematics Genealogy Project Edit this at Wikidata
  4. ^ a b www.stats.ox.ac.uk/~teh/ Edit this at Wikidata
  5. .
  6. ^ Yee Whye Teh at DBLP Bibliography Server Edit this at Wikidata
  7. ProQuest 305242430
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  8. .
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  10. ^ "On Bayesian Deep Learning and Deep Bayesian Learning". nips.cc.