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.
Countries citing papers authored by Christopher Ré
Since
Specialization
Citations
This map shows the geographic impact of Christopher Ré'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 Christopher Ré with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Ré more than expected).
This network shows the impact of papers produced by Christopher Ré. 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 Christopher Ré. The network helps show where Christopher Ré may publish in the future.
Co-authorship network of co-authors of Christopher Ré
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Ré.
A scholar is included among the top collaborators of Christopher Ré 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 Christopher Ré. Christopher Ré is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
De, Christopher, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, & Theodoros Rekatsinas. (2019). A Formal Framework for Probabilistic Unclean Databases.. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 18.9 indexed citations
5.
Xu, Peng, Bryan He, Christopher De, Ioannis Mitliagkas, & Christopher Ré. (2018). Accelerated Stochastic Power Iteration. International Conference on Artificial Intelligence and Statistics. 58–67.10 indexed citations
Yang, Jiyan, et al.. (2016). Sub-sampled Newton Methods with Non-uniform Sampling. Queensland's institutional digital repository (The University of Queensland). 29. 3000–3008.7 indexed citations
10.
Khamis, Mahmoud Abo, Hung Q. Ngo, Christopher Ré, & Atri Rudra. (2014). A Resolution-based Framework for Joins: Worst-case and Beyond. arXiv (Cornell University).1 indexed citations
11.
Anderson, Michael R., Victor Bittorf, Matthew Burgess, et al.. (2013). Brainwash: A data system for feature engineering. Conference on Innovative Data Systems Research.68 indexed citations
12.
Ngo, Hung Q., Dung T. Nguyen, Christopher Ré, & Atri Rudra. (2013). Towards Instance Optimal Join Algorithms for Data in Indexes. arXiv (Cornell University).1 indexed citations
13.
Bauer, Steven, Max Kleiman‐Weiner, Daniel A. Roberts, et al.. (2013). Evaluating Stream Filtering for Entity Profile Updates for TREC 2013.. Text REtrieval Conference.12 indexed citations
14.
Recht, Ben, Christopher Ré, Joel A. Tropp, & Victor Bittorf. (2012). Factoring nonnegative matrices with linear programs. Neural Information Processing Systems. 25. 1214–1222.83 indexed citations
15.
Recht, Benjamin & Christopher Ré. (2012). Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences. Conference on Learning Theory.20 indexed citations
Cafarella, Michael, Christopher Ré, Dan Suciu, Oren Etzioni, & Michele Banko. (2007). Structured querying of web text. Conference on Innovative Data Systems Research.21 indexed citations
18.
Ré, Christopher & Dan Suciu. (2007). Efficient Evaluation of.. 186–200.1 indexed citations
19.
Cafarella, Michael, Christopher Ré, Dan Suciu, & Oren Etzioni. (2007). Structured Querying of Web Text Data: A Technical Challenge.. Conference on Innovative Data Systems Research. 225–234.39 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.