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.
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
2004443 citationsCraig Boutilier, David Poole et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of David Poole'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 Poole with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Poole more than expected).
This network shows the impact of papers produced by David Poole. 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 Poole. The network helps show where David Poole may publish in the future.
Co-authorship network of co-authors of David Poole
This figure shows the co-authorship network connecting the top 25 collaborators of David Poole.
A scholar is included among the top collaborators of David Poole 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 Poole. David Poole is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kazemi, Seyed Mehran & David Poole. (2018). SimplE embedding for link prediction in knowledge graphs. Neural Information Processing Systems. 31. 4289–4300.66 indexed citations
4.
Poole, David, et al.. (2017). Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive.. Uncertainty in Artificial Intelligence.2 indexed citations
5.
Lukasiewicz, Thomas, María Vanina Martínez, David Poole, & Gerardo I. Simari. (2016). Probabilistic models over weighted orderings: fixed-parameter tractable variable elimination. Oxford University Research Archive (ORA) (University of Oxford). 494–503.3 indexed citations
6.
Poole, David, et al.. (2016). Negation without negation in probabilistic logic programming. Principles of Knowledge Representation and Reasoning. 529–532.1 indexed citations
7.
Poole, David, et al.. (2013). On integrating ontologies with relational probabilistic models. National Conference on Artificial Intelligence. 18–24.
8.
Poole, David, et al.. (2009). Lifted aggregation in directed first-order probabilistic models. International Joint Conference on Artificial Intelligence. 1922–1929.30 indexed citations
9.
Poole, David. (2007). Logical generative models for probabilistic reasoning about existence, roles and identity. National Conference on Artificial Intelligence. 1271–1277.5 indexed citations
10.
Sharma, Rita & David Poole. (2005). Probabilistic reasoning with hierarchically structured variables. International Joint Conference on Artificial Intelligence. 1391–1397.2 indexed citations
11.
Poole, David. (2003). First-order probabilistic inference. International Joint Conference on Artificial Intelligence. 985–991.232 indexed citations
12.
Morales-Menéndez, Rubén, Nando de Freitas, & David Poole. (2002). Real-Time Monitoring of Complex Industrial Processes with Particle Filters. Neural Information Processing Systems. 15. 1457–1464.44 indexed citations
13.
Gorniak, Peter & David Poole. (2000). Predicting Future User Actions by Observing Unmodified Applications. National Conference on Artificial Intelligence. 217–222.32 indexed citations
14.
Zhang, Nevin L. & David Poole. (1999). On the role of context-specific independence in probabilistic inference. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2. 1288–1293.18 indexed citations
15.
Poole, David. (1997). Probabilistic partial evaluation: exploiting rule structure in probabilistic inference. International Joint Conference on Artificial Intelligence. 1284–1291.30 indexed citations
16.
Boutilier, Craig & David Poole. (1996). Computing optimal policies for partially observable decision processes using compact representations. National Conference on Artificial Intelligence. 1168–1175.118 indexed citations
17.
Poole, David. (1995). Logic programming for robot control. International Joint Conference on Artificial Intelligence. 150–157.21 indexed citations
18.
Zhang, Nevin L. & David Poole. (1994). A simple approach to Bayesian network computations.172 indexed citations
19.
Poole, David, et al.. (1993). Hypothetically Speaking Default Reasoning and Discourse-Structure.. International Joint Conference on Artificial Intelligence. 1179–1185.3 indexed citations
20.
Poole, David. (1985). On the comparison of theories: preferring the most specific explanation. International Joint Conference on Artificial Intelligence. 144–147.117 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.