Michael Kearns
Impact in
- Artificial Intelligence top 0.05%
- Machine Learning and Algorithms
- Reinforcement Learning in Robotics
- Algorithms and Data Compression
- Machine Learning and Data Classification
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- Auction Theory and Applications
- Game Theory and Applications
- Advanced Bandit Algorithms Research
Papers in
-
- Machine Learning and Algorithms 70
- Algorithms and Data Compression 22
- Machine Learning and Data Classification 22
- Reinforcement Learning in Robotics 19
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- Game Theory and Applications 25
- Auction Theory and Applications 19
- Advanced Bandit Algorithms Research 15
- Co-authors
- Umesh Vazirani (2 shared papers)Leslie G. Valiant (6 shared papers)Satinder Singh (14 shared papers)Yishay Mansour (18 shared papers)Robert E. Schapire (16 shared papers)Aaron Roth (30 shared papers)Dana Ron (8 shared papers)Andrew Y. Ng (6 shared papers)
- Journals
- Machine Learning (10 papers)Proceedings of the National Academy of Sciences (6 papers)Information and Computation (4 papers)Journal of Computer and System Sciences (3 papers)Neural Computation (3 papers)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Michael Kearns
172 papers receiving 10.6k citations
Michael Kearns's Hit Papers
Peers
Comparison fields: 5 of 190
- Artificial Intelligence 6.9k
- Management Science and Operations Research 2.3k
- Computational Theory and Mathematics 1.9k
- Safety Research 723
- Computer Science Applications 362
Countries citing papers authored by Michael Kearns
This map shows the geographic impact of Michael Kearns'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 Michael Kearns with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Kearns more than expected).
Fields of papers citing papers by Michael Kearns
This network shows the impact of papers produced by Michael Kearns. 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 Michael Kearns. The network helps show where Michael Kearns may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Kearns, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 176 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Algorithmic Game Theory Hit paper breakdown → | 2007 | 1441 |
| 2 | An Introduction to Computational Learning Theory Hit paper breakdown → | 1994 | 851 |
| 3 | Fairness in Criminal Justice Risk Assessments: The State of the Art Hit paper breakdown → | 2018 | 520 |
| 4 | Near-Optimal Reinforcement Learning in Polynomial Time Hit paper breakdown → | 2002 | 393 |
| 5 | 1994 | 385 | |
| 6 | Proceedings of the 1997 conference on Advances in neural information processing systems 10 | 1998 | 330 |
| 7 | 1998 | 316 | |
| 8 | 1999 | 286 | |
| 9 | 1989 | 246 | |
| 10 | 2002 | 235 | |
| 11 | 2002 | 205 | |
| 12 | 1994 | 202 | |
| 13 | 1987 | 196 | |
| 14 | 1993 | 190 | |
| 15 | 1989 | 182 | |
| 16 | 1992 | 181 | |
| 17 | 2006 | 170 | |
| 18 | Nash convergence of gradient dynamics in general-sum games | 2000 | 152 |
| 19 | Computational Complexity of Machine Learning | 1990 | 142 |
| 20 | A sparse sampling algorithm for near-optimal planning in large Markov decision processes | 1999 | 130 |
About Michael Kearns
Michael Kearns is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Safety Research and Economics and Econometrics, having authored 176 papers that have together received 11.4k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (70 papers), Game Theory and Applications (25 papers), Algorithms and Data Compression (22 papers), Machine Learning and Data Classification (22 papers), Auction Theory and Applications (19 papers), Reinforcement Learning in Robotics (19 papers), Computability, Logic, AI Algorithms (15 papers) and Advanced Bandit Algorithms Research (15 papers). The work is most often cited by research in Artificial Intelligence (6.9k citations), Management Science and Operations Research (2.3k citations), Computational Theory and Mathematics (1.9k citations), Safety Research (723 citations) and Computer Science Applications (362 citations). Michael Kearns has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Umesh Vazirani, Leslie G. Valiant, Satinder Singh, Yishay Mansour, Robert E. Schapire, Aaron Roth, Dana Ron, Andrew Y. Ng, David Haussler and Hoda Heidari. Their work appears in journals such as Machine Learning, Proceedings of the National Academy of Sciences, Information and Computation, Journal of Computer and System Sciences and Neural Computation.
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.