Countries citing papers authored by Daniel Sheldon
Since
Specialization
Citations
This map shows the geographic impact of Daniel Sheldon'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 Daniel Sheldon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Sheldon more than expected).
This network shows the impact of papers produced by Daniel Sheldon. 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 Daniel Sheldon. The network helps show where Daniel Sheldon may publish in the future.
Co-authorship network of co-authors of Daniel Sheldon
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Sheldon.
A scholar is included among the top collaborators of Daniel Sheldon 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 Daniel Sheldon. Daniel Sheldon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
McKenna, Ryan & Daniel Sheldon. (2020). Permute-and-Flip: A new mechanism for differentially private selection. arXiv (Cornell University). 33. 193–203.1 indexed citations
Winner, Kevin, et al.. (2017). Exact Inference for Integer Latent-Variable Models. International Conference on Machine Learning. 3761–3770.
11.
Nguyen, Thien Huu, Akshat Kumar, Hoong Chuin Lau, & Daniel Sheldon. (2016). Approximate inference using DC programming for collective graphical models. International Conference on Artificial Intelligence and Statistics. 51. 685–693.4 indexed citations
12.
Winner, Kevin & Daniel Sheldon. (2016). Probabilistic Inference with Generating Functions for Poisson Latent Variable Models. Neural Information Processing Systems. 29. 2640–2648.
13.
Wu, Xiaojian, Daniel Sheldon, & Shlomo Zilberstein. (2015). FAST combinatorial algorithm for optimizing the spread of cascades. International Conference on Artificial Intelligence. 2655–2661.2 indexed citations
14.
Wu, Xiaojian, Daniel Sheldon, & Shlomo Zilberstein. (2014). Stochastic Network Design in Bidirected Trees. Neural Information Processing Systems. 27. 882–890.7 indexed citations
15.
Liu, Liping, Daniel Sheldon, & Thomas G. Dietterich. (2014). Gaussian Approximation of Collective Graphical Models. International Conference on Machine Learning. 1602–1610.2 indexed citations
16.
Sheldon, Daniel, et al.. (2013). Approximate Inference in Collective Graphical Models. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 28(3). 1004–1012.22 indexed citations
17.
Wu, Xiaojian, Akshat Kumar, Daniel Sheldon, & Shlomo Zilberstein. (2013). Parameter learning for latent network diffusion. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 2923–2930.5 indexed citations
Kozen, Dexter, et al.. (2007). Collective Inference on Markov Models for Modeling Bird Migration. Neural Information Processing Systems. 20. 1321–1328.20 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.