Max Chickering
- Artificial Intelligence top 1%
- Information Systems top 2%
- Management Science and Operations Research top 2%
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 5%
- Co-authors
- Christopher MeekJoseph Y. HalpernDavid HeckermanDan GeigerPatrice SimardSteven M. DruckerSaleema AmershiBongshin Lee
- Topics
- Bayesian Modeling and Causal Inference (7 papers)Optimization and Search Problems (3 papers)Auction Theory and Applications (3 papers)
- Journals
- Machine LearningElectronic Notes in Discrete MathematicsKnowledge Discovery and Data Mining
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Max Chickering
18 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Artificial Intelligence 1.1k
- Information Systems 430
- Management Science and Operations Research 277
- Computer Vision and Pattern Recognition 241
- Computational Theory and Mathematics 156
Countries citing papers authored by Max Chickering
This map shows the geographic impact of Max 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 Max Chickering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Chickering more than expected).
Fields of papers citing papers by Max Chickering
This network shows the impact of papers produced by Max 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 Max Chickering. The network helps show where Max Chickering may publish in the future.
Co-authorship network of co-authors of Max Chickering
This figure shows the co-authorship network connecting the top 25 collaborators of Max Chickering. A scholar is included among the top collaborators of Max Chickering 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 Max Chickering. Max Chickering is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 174 | |
| 2 | 10 | |
| 3 | 10 | |
| 4 | 19 | |
| 5 | 9 | |
| 6 | Proceedings of the ACM SIGKDD Workshop on Human Computation | 1 |
| 7 | 11 | |
| 8 | 22 | |
| 9 | Stochastic and Contingent Payment Auctions | 14 |
| 10 | Proceedings of the 20th conference on Uncertainty in artificial intelligencebreakdown → | 1095 |
| 11 | Uncertainty in artificial intelligence : proceedings of the Twentieth Conference (2004) : July 7-11, 2004, Banff, Canada | 1 |
| 12 | Monotone DAG Faithfulness: A Bad Assumption | 4 |
| 13 | The WinMine Toolkit | 64 |
| 14 | 4 | |
| 15 | Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization | 21 |
| 16 | Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network | 8 |
| 17 | Learning Bayesian Networks is NP-Complete | 61 |
| 18 | Learning Bayesian Networks: Search Methods and Experimental Results | 123 |
About Max Chickering
Max Chickering is a scholar working on General Decision Sciences, Management Science and Operations Research and Computer Science Applications, having authored 18 papers that have together received 1.7k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (7 papers), Optimization and Search Problems (3 papers) and Auction Theory and Applications (3 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Management Science and Operations Research (277 citations) and Information Systems (430 citations). Max Chickering has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Christopher Meek, Joseph Y. Halpern, David Heckerman, Dan Geiger, Patrice Simard, Steven M. Drucker, Saleema Amershi, Bongshin Lee, Jina Suh and Denis Charles. Their work appears in journals such as Machine Learning, Electronic Notes in Discrete Mathematics and Knowledge Discovery and Data Mining.
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.