Christopher Meek

7.0k citations
81 papers · 3.5k · 1 hit paper · h-index 31

Impact in

    • Topic Modeling
    • Bayesian Modeling and Causal Inference
    • Natural Language Processing Techniques
    • Adversarial Robustness in Machine Learning
    • Data Management and Algorithms
    • Advanced Malware Detection Techniques

Papers in

    • Bayesian Modeling and Causal Inference 27
    • Topic Modeling 14
    • Machine Learning and Algorithms 12
    • Bayesian Methods and Mixture Models 10
    • Natural Language Processing Techniques 10
    • Text and Document Classification Technologies 5
    • Recommender Systems and Techniques 6

Christopher Meek

74 papers receiving 3.2k citations

Christopher Meek's Hit Papers

Adversarial learning 2005 · 402 citations
4020+7+14Years since publication100200300400

Peers

Christopher Meek
Comparison fields: 5 of 144
  • Artificial Intelligence 2.3k
  • Signal Processing 670
  • Computational Mathematics 34
  • Information Systems 996
  • Management Science and Operations Research 318
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Christopher Meek relative to Ralf Herbrich United Kingdom Ralf Herbrich's profile →
Citations per field
00.5×1.5×1.9×
Ralf Herbrich · 1×
Citations per year

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Christopher Meek, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Christopher Meek Line = papers co-authored together Christopher Meek links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Adversarial learning
Hit paper breakdown →
2005402
2 2014263
3 2004177
4 2000177
5 2005150
6
Question Answering Using Enhanced Lexical Semantic Models
2013139
7 2003138
8 1998121
9 2014118
10 2009105
11
Learning Bayesian networks with discrete variables from data
1995101
12 200678
13 199477
14
Improving similarity measures for short segments of text
200775
15 200173
16 201071
17 200170
18 201268
19 199868
20 200066

About Christopher Meek

Christopher Meek is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 81 papers that have together received 3.5k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (27 papers), Topic Modeling (14 papers), Machine Learning and Algorithms (12 papers), Data Management and Algorithms (11 papers), Bayesian Methods and Mixture Models (10 papers), Natural Language Processing Techniques (10 papers), Recommender Systems and Techniques (6 papers) and Text and Document Classification Technologies (5 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Signal Processing (670 citations), Computational Mathematics (34 citations), Information Systems (996 citations) and Management Science and Operations Research (318 citations). Christopher Meek has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Daniel Lowd, Wen-tau Yih, David Heckerman, Asela Gunawardana, Peter Spirtes, Clark Glymour, Xiaodong He, Dan Geiger, Igor V. Cadez and Padhraic Smyth. Their work appears in journals such as Bioinformatics, Data Mining and Knowledge Discovery, Multivariate Behavioral Research, Journal of Artificial Intelligence Research and The Annals of Statistics.

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.

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