Yee-Whye Teh
E80657
Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Yee-Whye Teh canonical | 4 |
| Yee Whye Teh | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T645516 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yee-Whye Teh Context triple: [A fast learning algorithm for deep belief nets, author, Yee-Whye Teh]
-
A.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
B.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
C.
Quoc V. Le
Quoc V. Le is a prominent computer scientist and AI researcher known for his influential work in deep learning and large-scale machine learning at Google.
-
D.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
-
E.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yee-Whye Teh Target entity description: Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
-
A.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
B.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
C.
Quoc V. Le
Quoc V. Le is a prominent computer scientist and AI researcher known for his influential work in deep learning and large-scale machine learning at Google.
-
D.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
-
E.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
machine learning researcher
ⓘ
person ⓘ statistician ⓘ |
| affiliation | Oxford-Man Institute of Quantitative Finance ⓘ |
| countryOfCitizenship | Malaysia ⓘ |
| educatedAt |
Cambridge University
ⓘ
surface form:
University of Cambridge
Yale University ⓘ |
| employer | University of Oxford ⓘ |
| ethnicGroup | Chinese Malaysian ⓘ |
| fieldOfWork |
Bayesian nonparametrics
ⓘ
Bayesian statistics ⓘ deep learning ⓘ machine learning ⓘ probabilistic modeling ⓘ statistics ⓘ |
| hasAcademicAdvisor | Radford M. Neal ⓘ |
| hasAcademicRank | Professor of Statistical Machine Learning ⓘ |
| hasResearchArea |
Bayesian deep learning
ⓘ
graphical models ⓘ probabilistic machine learning ⓘ stochastic processes in machine learning ⓘ |
| hasRole |
academic researcher
ⓘ
doctoral supervisor ⓘ |
| hasWrittenWork |
Collapsed variational inference for HDP
ⓘ
Dirichlet process models ⓘ
surface form:
Hierarchical Dirichlet Processes
Stick-breaking construction for the Indian buffet process ⓘ |
| knownFor |
Bayesian nonparametric methods
ⓘ
Dirichlet process models ⓘ contributions to deep generative models ⓘ Dirichlet process models ⓘ
surface form:
hierarchical Dirichlet processes
probabilistic topic models ⓘ |
| languageSpoken | English ⓘ |
| memberOf |
Department of Statistics
ⓘ
surface form:
Department of Statistics, University of Oxford
|
| notableStudent | Diederik P. Kingma ⓘ |
| notableWork |
applications of Bayesian nonparametrics in machine learning
ⓘ
development of hierarchical Dirichlet process topic models ⓘ |
| occupation | professor ⓘ |
| researchInterest |
Markov chain Monte Carlo
ⓘ
deep generative models ⓘ nonparametric Bayesian methods ⓘ unsupervised learning ⓘ variational inference ⓘ |
| workLocation | Oxford ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Yee-Whye Teh Description of subject: Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
A fast learning algorithm for deep belief nets
this entity surface form:
Yee Whye Teh