David Maxwell Chickering

3.8k citations
37 papers · 2.0k · 1 hit paper · h-index 18

Impact in

Papers in

David Maxwell Chickering

36 papers receiving 1.9k citations

David Maxwell Chickering's Hit Papers

Learning Bayesian Networks is NP-Complete 1996 · 716 citations
7160+10+20Years since publication200400600

Peers

David Maxwell Chickering
Comparison fields: 5 of 115
  • Artificial Intelligence 1.4k
  • Management Science and Operations Research 355
  • Information Systems 468
  • Signal Processing 225
  • Statistics and Probability 145
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Juan F. Huete Spain
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Jee-Hyong Lee South Korea
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Countries citing papers authored by David Maxwell Chickering

Since Specialization
Citations

This map shows the geographic impact of David Maxwell Chickering'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 Maxwell Chickering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Maxwell Chickering more than expected).

Fields of papers citing papers by David Maxwell Chickering

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Maxwell Chickering. 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 Maxwell Chickering. The network helps show where David Maxwell Chickering may publish in the future.

Co-authors

The 17 scholars most cited alongside David Maxwell Chickering, 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 David Maxwell Chickering Line = papers co-authored together David Maxwell Chickering links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Learning Bayesian Networks is NP-Complete
Hit paper breakdown →
1996716
2
Learning equivalence classes of Bayesian network structures
1996240
3 1997222
4 2008118
5 201381
6 201274
7 201369
8 200866
9 200347
10 199642
11 201239
12
Improving Cloaking Detection using Search Query Popularity and Monetizability.
200634
13 200733
14 200023
15 200022
16 200919
17 200519
18 201318
19
Modeling contextual factors of click rates
200717
20
Learning Bayesian Networks From Dependency Networks: A Preliminary Study
200316

About David Maxwell Chickering

David Maxwell Chickering is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Marketing and Computer Vision and Pattern Recognition, having authored 37 papers that have together received 2.0k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (17 papers), Data Quality and Management (8 papers), Consumer Market Behavior and Pricing (5 papers), Speech and dialogue systems (5 papers), Machine Learning and Algorithms (5 papers), Topic Modeling (5 papers), Bayesian Methods and Mixture Models (5 papers) and Statistical Methods and Bayesian Inference (4 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Management Science and Operations Research (355 citations), Information Systems (468 citations), Signal Processing (225 citations) and Statistics and Probability (145 citations). David Maxwell Chickering has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include David Heckerman, Christopher Meek, Ben Carterette, Susan Dumais, Christopher Meek, Tim Paek, Richard E. Korf, Kumar Chellapilla, Paul N. Bennett and Anton Mityagin. Their work appears in journals such as User Modeling and User-Adapted Interaction, Artificial Intelligence, INFORMS Journal on Applied Analytics, Journal of Machine Learning Research and Machine Learning.

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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