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.
Adversarial learning
2005402 citationsDaniel Lowd, Christopher Meekprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Christopher Meek
Since
Specialization
Citations
This map shows the geographic impact of Christopher Meek'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 Meek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Meek more than expected).
Fields of papers citing papers by Christopher Meek
This network shows the impact of papers produced by Christopher Meek. 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 Meek. The network helps show where Christopher Meek may publish in the future.
Co-authorship network of co-authors of Christopher Meek
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Meek.
A scholar is included among the top collaborators of Christopher Meek 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 Meek. Christopher Meek 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.
Yu, Tao, et al.. (2021). SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing. International Conference on Learning Representations.30 indexed citations
2.
Gunawardana, Asela & Christopher Meek. (2016). Universal Models of Multivariate Temporal Point Processes. International Conference on Artificial Intelligence and Statistics. 556–563.10 indexed citations
3.
Chickering, David Maxwell & Christopher Meek. (2015). Selective Greedy Equivalence Search: finding optimal Bayesian networks using a polynomial number of score evaluations. Uncertainty in Artificial Intelligence. 211–219.4 indexed citations
Yih, Wen-tau, Ming‐Wei Chang, Christopher Meek, & Andrzej Pastusiak. (2013). Question Answering Using Enhanced Lexical Semantic Models. Meeting of the Association for Computational Linguistics. 1744–1753.139 indexed citations
6.
Yih, Wen-tau, et al.. (2013). Combining Heterogeneous Models for Measuring Relational Similarity. North American Chapter of the Association for Computational Linguistics. 1000–1009.29 indexed citations
7.
Zweig, Geoffrey, John Platt, Christopher Meek, et al.. (2012). Computational Approaches to Sentence Completion. Meeting of the Association for Computational Linguistics. 601–610.19 indexed citations
8.
Gunawardana, Asela, Christopher Meek, & Puyang Xu. (2011). A Model for Temporal Dependencies in Event Streams. Neural Information Processing Systems. 24. 1962–1970.49 indexed citations
9.
Wexler, Ydo & Christopher Meek. (2008). MAS: a multiplicative approximation scheme for probabilistic inference. Neural Information Processing Systems. 21. 1761–1768.4 indexed citations
10.
Becker, Hila, Christopher Meek, & David Maxwell Chickering. (2007). Modeling contextual factors of click rates. National Conference on Artificial Intelligence. 1310–1315.15 indexed citations
11.
Yih, Wen-tau & Christopher Meek. (2007). Improving similarity measures for short segments of text. National Conference on Artificial Intelligence. 1489–1494.75 indexed citations
Thiesson, Bo & Christopher Meek. (2005). Efficient Gradient Computation for Conditional Gaussian Models.. International Conference on Artificial Intelligence and Statistics.
15.
Geiger, Dan & Christopher Meek. (2005). Structured Variational Inference Procedures and their Realizations.. International Conference on Artificial Intelligence and Statistics.7 indexed citations
16.
Kjærulff, Uffe, Christopher Meek, Adnan Darwiche, & Nir Friedman. (2003). Uncertainty in artificial intelligence : proceedings of the nineteenth conference (2003), August 7-10, 2003, Acapulco, Mexico.12 indexed citations
17.
Meek, Christopher, et al.. (2000). Challenges of the Email Domain for Text Classification. International Conference on Machine Learning. 103–110.48 indexed citations
18.
Geiger, Dan, et al.. (1999). On the geometry of DAG models with hidden variables.. International Conference on Artificial Intelligence and Statistics.3 indexed citations
19.
Spirtes, Peter & Christopher Meek. (1995). Learning Bayesian networks with discrete variables from data. Knowledge Discovery and Data Mining. 76(4). 294–299.101 indexed citations
20.
Scheines, Richard, Peter Spirtes, Clark Glymour, & Christopher Meek. (1994). TETRAD II : tools for causal modeling.46 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.