This map shows the geographic impact of Arthur Choi'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 Arthur Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arthur Choi more than expected).
This network shows the impact of papers produced by Arthur Choi. 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 Arthur Choi. The network helps show where Arthur Choi may publish in the future.
Co-authorship network of co-authors of Arthur Choi
This figure shows the co-authorship network connecting the top 25 collaborators of Arthur Choi.
A scholar is included among the top collaborators of Arthur Choi 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 Arthur Choi. Arthur Choi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Choi, Arthur, et al.. (2020). A New Perspective on Learning Context-Specific Independence. arXiv (Cornell University).1 indexed citations
3.
Choi, Arthur, et al.. (2020). Supervised Learning with Background Knowledge.. 89–100.
4.
Huang, Haiying, et al.. (2019). Conditional Independence in Testing Bayesian Networks. International Conference on Machine Learning. 5701–5709.
5.
Shih, Andy, Arthur Choi, & Adnan Darwiche. (2018). Formal Verification of Bayesian Network Classifiers. 427–438.11 indexed citations
6.
Choi, Arthur & Adnan Darwiche. (2017). On Relaxing Determinism in Arithmetic Circuits. International Conference on Machine Learning. 825–833.1 indexed citations
7.
Choi, Arthur, et al.. (2017). A Tractable Probabilistic Model for Subset Selection.. Uncertainty in Artificial Intelligence.4 indexed citations
8.
Choi, Arthur, et al.. (2017). Tractability in Structured Probability Spaces. Neural Information Processing Systems. 30. 3477–3485.5 indexed citations
9.
Choi, Arthur, et al.. (2016). Tractable Operations for Arithmetic Circuits of Probabilistic Models. Neural Information Processing Systems. 29. 3936–3944.15 indexed citations
10.
Choi, Arthur, et al.. (2016). Learning Bayesian networks with ancestral constraints. Neural Information Processing Systems. 29. 2325–2333.13 indexed citations
11.
Choi, Arthur, et al.. (2016). Enumerating Equivalence Classes of Bayesian Networks using EC Graphs. International Conference on Artificial Intelligence and Statistics. 591–599.6 indexed citations
12.
Choi, Arthur, Guy Van den Broeck, & Adnan Darwiche. (2015). Probability Distributions over Structured Spaces.. Lirias (KU Leuven).
13.
Davis, Jesse, et al.. (2015). Tractable learning for complex probability queries. Lirias (KU Leuven). 28. 2242–2250.18 indexed citations
14.
Broeck, Guy Van den, et al.. (2014). Probabilistic sentential decision diagrams: Learning with massive logical constraints. Lirias (KU Leuven). 1–9.6 indexed citations
15.
Mohan, Karthika, Guy Van den Broeck, Arthur Choi, & Judea Pearl. (2014). An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data. International Conference on Machine Learning.3 indexed citations
Choi, Arthur & Adnan Darwiche. (2009). Relax then compensate: on max-product belief propagation and more. Neural Information Processing Systems. 351–359.4 indexed citations
19.
Choi, Arthur & Adnan Darwiche. (2009). Approximating MAP by Compensating for Structural Relaxations. Neural Information Processing Systems. 22. 351–359.1 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.