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 David Barber'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 David Barber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Barber more than expected).
This network shows the impact of papers produced by David Barber. 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 David Barber. The network helps show where David Barber may publish in the future.
Co-authorship network of co-authors of David Barber
This figure shows the co-authorship network connecting the top 25 collaborators of David Barber.
A scholar is included among the top collaborators of David Barber 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 David Barber. David Barber is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Townsend, James T., et al.. (2019). Practical lossless compression with latent variables using bits back coding. UCL Discovery (University College London).6 indexed citations
3.
Ritter, Hippolyt, Aleksandar Botev, & David Barber. (2018). Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. UCL Discovery (University College London). 31. 3738–3748.26 indexed citations
4.
Karpinski, Stefan, et al.. (2018). On Machine Learning and Programming Languages. UCL Discovery (University College London).6 indexed citations
5.
Ritter, Hippolyt, Aleksandar Botev, & David Barber. (2018). A Scalable Laplace Approximation for Neural Networks. UCL Discovery (University College London).51 indexed citations
6.
Botev, Aleksandar, Bowen Zheng, & David Barber. (2017). Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. UCL Discovery (University College London). 1030–1038.8 indexed citations
7.
Lever, Guy, et al.. (2016). Approximate Newton methods for policy search in Markov decision processes. Journal of Machine Learning Research. 17(1). 8055–8105.6 indexed citations
8.
Barber, David & Yali Wang. (2014). Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. International Conference on Machine Learning. 1485–1493.21 indexed citations
9.
Barber, David, et al.. (2012). Affine Independent Variational Inference. UCL Discovery (University College London). 25. 2186–2194.7 indexed citations
10.
Barber, David, et al.. (2011). Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. UCL Discovery (University College London). 199–207.13 indexed citations
11.
Barber, David, et al.. (2010). Variational methods for Reinforcement Learning. UCL Discovery (University College London). 241–248.17 indexed citations
Chiappa, Silvia & David Barber. (2005). Generative Independent Component Analysis for EEG Classification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 297–302.5 indexed citations
14.
Barber, David & Felix Agakov. (2003). Information Maximization in Noisy Channels : A Variational Approach. Neural Information Processing Systems. 16. 201–208.4 indexed citations
15.
Barber, David. (2002). Learning in Spiking Neural Assemblies. UCL Discovery (University College London). 15. 165–172.13 indexed citations
16.
Barber, David. (2002). Dynamic Bayesian Networks with Deterministic Latent Tables. Neural Information Processing Systems. 15. 729–736.4 indexed citations
Barber, David, et al.. (1997). Radial Basis Functions: A Bayesian Treatment. Neural Information Processing Systems. 10. 402–408.9 indexed citations
19.
Barber, David & Christopher K. I. Williams. (1996). Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. Aston Publications Explorer (Aston University). 9. 340–346.35 indexed citations
20.
Sollich, Peter & David Barber. (1996). Online Learning from Finite Training Sets: An Analytical Case Study. Neural Information Processing Systems. 9. 274–280.2 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.