Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Yee Whye Teh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yee Whye Teh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yee Whye Teh more than expected).
This network shows the impact of papers produced by Yee Whye Teh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yee Whye Teh. The network helps show where Yee Whye Teh may publish in the future.
Co-authorship network of co-authors of Yee Whye Teh
This figure shows the co-authorship network connecting the top 25 collaborators of Yee Whye Teh.
A scholar is included among the top collaborators of Yee Whye Teh based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Yee Whye Teh. Yee Whye Teh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Brauner, Jan, Sören Mindermann, Mrinank Sharma, et al.. (2020). Inferring the effectiveness of government interventions against COVID-19. Science. 371(6531).630 indexed citations breakdown →
Amersfoort, Joost van, Lewis Smith, Yee Whye Teh, & Yarin Gal. (2020). Uncertainty Estimation Using a Single Deep Deterministic Neural Network. International Conference on Machine Learning. 9690–9700.14 indexed citations
4.
Xu, Jin, et al.. (2020). MetaFun: Meta-Learning with Iterative Functional Updates. Oxford University Research Archive (ORA) (University of Oxford). 1. 10617–10627.11 indexed citations
5.
Lakshminarayanan, Balaji, et al.. (2020). Bayesian Deep Ensembles via the Neural Tangent Kernel. Neural Information Processing Systems. 33. 1010–1022.1 indexed citations
6.
Mathieu, Émile, Tom Rainforth, N. Siddharth, & Yee Whye Teh. (2018). Disentangling Disentanglement. arXiv (Cornell University).3 indexed citations
7.
Miscouridou, Xenia, François Caron, & Yee Whye Teh. (2018). Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data. Oxford University Research Archive (ORA) (University of Oxford). 31. 2343–2352.3 indexed citations
8.
Chen, Jianfei, Jun Zhu, Yee Whye Teh, & Tong Zhang. (2018). Stochastic Expectation Maximization with Variance Reduction. Oxford University Research Archive (ORA) (University of Oxford). 31. 7967–7977.12 indexed citations
9.
Sejdinović, Dino, et al.. (2017). Deep Kernel Machines via the Kernel Reparametrization Trick. International Conference on Learning Representations.1 indexed citations
10.
Sejdinović, Dino, et al.. (2016). DR-ABC: approximate Bayesian computation with kernel-based distribution regression. Oxford University Research Archive (ORA) (University of Oxford). 1482–1491.5 indexed citations
Lakshminarayanan, Balaji, et al.. (2014). Distributed Bayesian Posterior Sampling via Moment Sharing. UCL Discovery (University College London). 27. 3356–3364.21 indexed citations
13.
Teh, Yee Whye, et al.. (2013). Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex. Neural Information Processing Systems. 26. 3102–3110.72 indexed citations
14.
Rao, Vinayak & Yee Whye Teh. (2012). MCMC for continuous-time discrete-state systems. Neural Information Processing Systems. 25. 701–709.11 indexed citations
15.
Rao, Vinayak & Yee Whye Teh. (2009). Spatial Normalized Gamma Processes. Oxford University Research Archive (ORA) (University of Oxford). 22. 1554–1562.29 indexed citations
16.
Görür, Dilan & Yee Whye Teh. (2008). An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering. UCL Discovery (University College London). 21. 521–528.13 indexed citations
17.
Teh, Yee Whye, Kenichi Kurihara, & Max Welling. (2007). Collapsed Variational Inference for HDP. UCL Discovery (University College London). 20. 1481–1488.86 indexed citations
18.
Teh, Yee Whye, Dilan Görür, & Zoubin Ghahramani. (2007). Stick-breaking Construction for the Indian Buffet Process. UCL Discovery (University College London). 556–563.123 indexed citations
19.
Kurihara, Kenichi, Max Welling, & Yee Whye Teh. (2007). Collapsed variational Dirichlet process mixture models. UCL Discovery (University College London). 2796–2801.107 indexed citations
20.
Teh, Yee Whye & Max Welling. (2003). On Improving the Efficiency of the Iterative Proportional Fitting Procedure. International Conference on Artificial Intelligence and Statistics. 262–269.15 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.