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.
MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks
This map shows the geographic impact of David Jensen'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 Jensen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Jensen more than expected).
This network shows the impact of papers produced by David Jensen. 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 Jensen. The network helps show where David Jensen may publish in the future.
Co-authorship network of co-authors of David Jensen
This figure shows the co-authorship network connecting the top 25 collaborators of David Jensen.
A scholar is included among the top collaborators of David Jensen 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 Jensen. David Jensen 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.
Jensen, David, et al.. (2020). Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. International Conference on Learning Representations.4 indexed citations
Jensen, David, et al.. (2019). Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL. arXiv (Cornell University).2 indexed citations
4.
Jensen, David, et al.. (2016). Inferring causal direction from relational data. Uncertainty in Artificial Intelligence. 12–21.1 indexed citations
5.
Jensen, David, et al.. (2015). Using supervised learning to uncover deep musical structure. National Conference on Artificial Intelligence. 1770–1776.3 indexed citations
6.
Friedland, Lisa, David Jensen, & Michael Lavine. (2013). Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events. International Conference on Machine Learning. 1175–1183.3 indexed citations
7.
Jensen, David, et al.. (2006). The NFL Coaching Network: Analysis of the Social Network among Professional Football Coaches.. National Conference on Artificial Intelligence. 112–119.15 indexed citations
8.
Hart, Stephen, Roderic A. Grupen, & David Jensen. (2005). A relational representation for procedural task knowledge. Defense Technical Information Center (DTIC). 1280–1285.14 indexed citations
9.
Şimşek, Özgür & David Jensen. (2005). Decentralized search in networks using homophily and degree disparity. International Joint Conference on Artificial Intelligence. 304–310.26 indexed citations
Jensen, David, Jennifer Neville, & Michael Hay. (2003). Avoiding bias when aggregating relational data with degree disparity. International Conference on Machine Learning. 274–281.22 indexed citations
12.
McCallum, Andrew & David Jensen. (2003). A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models. ScholarWorks@UMassAmherst (University of Massachusetts Amherst).37 indexed citations
13.
Jensen, David & Jennifer Neville. (2002). Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. International Conference on Machine Learning. 259–266.111 indexed citations
14.
Lavrenko, Victor, et al.. (2000). Mining of Concurrent Text and Time Series.74 indexed citations
15.
Jensen, David, Michael Atighetchi, Régis Vincent, & Victor Lesser. (1999). Learning quantitative knowledge for multiagent coordination. National Conference on Artificial Intelligence. 24–31.23 indexed citations
16.
Oates, Tim & David Jensen. (1999). Toward a theoretical understanding of why and when decision tree pruning algorithms fail. National Conference on Artificial Intelligence. 372–378.13 indexed citations
17.
Oates, Tim & David Jensen. (1998). Large datasets lead to overly complex models: an explanation and a solution. Knowledge Discovery and Data Mining. 294–298.53 indexed citations
18.
Jensen, David, et al.. (1997). Adjusting for multiple comparisons in decision tree pruning. Knowledge Discovery and Data Mining. 195–198.14 indexed citations
19.
Oates, Tim & David Jensen. (1997). The Effects of Training Set Size on Decision Tree Complexity. International Conference on Machine Learning. 254–262.82 indexed citations
20.
Jensen, David. (1997). Prospective Assessment of AI Technologies for Fraud Detection: A Case Study.10 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.