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.
Countries citing papers authored by Edwin V. Bonilla
Since
Specialization
Citations
This map shows the geographic impact of Edwin V. Bonilla'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 Edwin V. Bonilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edwin V. Bonilla more than expected).
Fields of papers citing papers by Edwin V. Bonilla
This network shows the impact of papers produced by Edwin V. Bonilla. 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 Edwin V. Bonilla. The network helps show where Edwin V. Bonilla may publish in the future.
Co-authorship network of co-authors of Edwin V. Bonilla
This figure shows the co-authorship network connecting the top 25 collaborators of Edwin V. Bonilla.
A scholar is included among the top collaborators of Edwin V. Bonilla 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 Edwin V. Bonilla. Edwin V. Bonilla 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.
Damoulas, Theodoros, et al.. (2021). Distribution Regression for Sequential Data. Warwick Research Archive Portal (University of Warwick). 3754–3762.
2.
Elinas, Pantelis, et al.. (2020). Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. Neural Information Processing Systems. 33. 18648–18660.8 indexed citations
3.
Bonilla, Edwin V., Karl Krauth, & Amir Dezfouli. (2019). Generic Inference in Latent Gaussian Process Models. Journal of Machine Learning Research. 20(117). 1–63.3 indexed citations
Dezfouli, Amir & Edwin V. Bonilla. (2015). Scalable inference for Gaussian process models with black-box likelihoods. Neural Information Processing Systems. 28. 1414–1422.18 indexed citations
6.
Steinberg, Daniel & Edwin V. Bonilla. (2014). Extended and Unscented Gaussian Processes. Neural Information Processing Systems. 27. 1251–1259.3 indexed citations
Bonilla, Edwin V., et al.. (2014). Collaborative multi-output Gaussian processes. ANU Open Research (Australian National University). 643–652.29 indexed citations
9.
Bonilla, Edwin V., et al.. (2014). Automated Variational Inference for Gaussian Process Models. Neural Information Processing Systems. 27. 1404–1412.10 indexed citations
Abbasnejad, Ehsan, Scott Sanner, Edwin V. Bonilla, & Pascal Poupart. (2013). Learning community-based preferences via dirichlet process mixtures of Gaussian processes. ANU Open Research (Australian National University). 1213–1219.13 indexed citations
12.
O’Callaghan, Simon, et al.. (2013). Bayesian joint inversions for the exploration of earth resources. International Joint Conference on Artificial Intelligence. 2877–2884.7 indexed citations
13.
Ramos, Fábio, et al.. (2012). Bayesian data fusion for geothermal exploration. ANU Open Research (Australian National University).3 indexed citations
14.
Newman, David, Edwin V. Bonilla, & Wray Buntine. (2011). Improving Topic Coherence with Regularized Topic Models. ANU Open Research (Australian National University). 24. 496–504.109 indexed citations
15.
Guo, Shengbo, Scott Sanner, & Edwin V. Bonilla. (2010). Gaussian Process Preference Elicitation. Neural Information Processing Systems. 23. 262–270.38 indexed citations
Fursin, Grigori, Olivier Temam, Mircea Namolaru, et al.. (2008). Proceedings of the GCC Developers' Summit.88 indexed citations
18.
Bonilla, Edwin V., Felix Agakov, & Christopher K. I. Williams. (2007). Kernel Multi-task Learning using Task-specific Features. Edinburgh Research Explorer (University of Edinburgh). 43–50.51 indexed citations
19.
Cavazos, John, Grigori Fursin, Felix Agakov, et al.. (2007). Code Generation and Optimization, 2007. CGO '07. International Symposium on.102 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.