This map shows the geographic impact of Ben London'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 Ben London with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben London more than expected).
This network shows the impact of papers produced by Ben London. 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 Ben London. The network helps show where Ben London may publish in the future.
Co-authorship network of co-authors of Ben London
This figure shows the co-authorship network connecting the top 25 collaborators of Ben London.
A scholar is included among the top collaborators of Ben London 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 Ben London. Ben London is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Joachims, Thorsten, Ben London, Yi Su, Adith Swaminathan, & Lequn Wang. (2021). Recommendations as treatments. AI Magazine. 42(3). 19–30.7 indexed citations
London, Ben. (2017). A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent. Neural Information Processing Systems. 30. 2931–2940.8 indexed citations
5.
London, Ben. (2017). Generalization Bounds for Randomized Learning with Application to Stochastic Gradient Descent.2 indexed citations
6.
London, Ben, Bert Huang, & Lise Getoor. (2016). Stability and generalization in structured prediction. Journal of Machine Learning Research. 17(221). 7808–7859.15 indexed citations
7.
London, Ben & Alex Schwing. (2016). Generative Adversarial Structured Networks.1 indexed citations
8.
Namata, Galileo, Ben London, & Lise Getoor. (2016). Collective Graph Identification. ACM Transactions on Knowledge Discovery from Data. 10(3). 1–36.9 indexed citations
9.
Pujara, Jay, Ben London, & Lise Getoor. (2015). Budgeted online collective inference. Uncertainty in Artificial Intelligence. 712–721.3 indexed citations
10.
London, Ben, Bert Huang, & Lise Getoor. (2015). The Benefits of Learning with Strongly Convex Approximate Inference. 410–418.2 indexed citations
11.
London, Ben, Bert Huang, Ben Taskar, & Lise Getoor. (2014). {PAC-Bayesian Collective Stability}. International Conference on Artificial Intelligence and Statistics. 585–594.7 indexed citations
12.
London, Ben & Lise Getoor. (2014). Collective Classification of Network Data.. 399–416.17 indexed citations
13.
Bach, Stephen H., Bert Huang, Ben London, & Lise Getoor. (2013). Hinge-loss Markov random fields: convex inference for structured prediction. arXiv (Cornell University). 32–41.33 indexed citations
14.
London, Ben, Bert Huang, & Lise Getoor. (2013). Improved Generalization Bounds for Large-scale Structured Prediction.1 indexed citations
London, Ben, Bert Huang, Ben Taskar, & Lise Getoor. (2013). Collective Stability in Structured Prediction: Generalization from One Example. 828–836.11 indexed citations
17.
Namata, Galileo, Ben London, Lise Getoor, & Bert Huang. (2012). Query-driven active surveying for collective classification.100 indexed citations
18.
Pujara, Jay, Ben London, & Lise Getoor. (2011). Reducing Label Cost by Combining Feature Labels and Crowdsourcing.6 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.