Mark Sellke
Impact in
- Statistics and Probability top 10%
- Markov Chains and Monte Carlo Methods
- Random Matrices and Applications
Papers in
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- Theoretical and Computational Physics 10
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- Machine Learning and Algorithms 4
- Co-authors
- A. El Alaoui (5 shared papers)Andrea Montanari (4 shared papers)Aleksandrs Slivkins (3 shared papers)Victoria Kostina (3 shared papers)Gireeja Ranade (3 shared papers)Yuval Peres (3 shared papers)Sébastien Bubeck (1 shared paper)Yin Tat Lee (1 shared paper)
- Journals
- Communications in Mathematical Physics (2 papers)Communications on Pure and Applied Mathematics (2 papers)Journal of Statistical Physics (2 papers)Operations Research (1 paper)The Annals of Probability (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Mark Sellke
22 papers receiving 140 citations
Peers
Comparison fields: 5 of 48
- Statistics and Probability 26
- Computer Graphics and Computer-Aided Design 11
- Condensed Matter Physics 35
- Mathematical Physics 20
- Computational Theory and Mathematics 22
Countries citing papers authored by Mark Sellke
This map shows the geographic impact of Mark Sellke'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 Mark Sellke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Sellke more than expected).
Fields of papers citing papers by Mark Sellke
This network shows the impact of papers produced by Mark Sellke. 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 Mark Sellke. The network helps show where Mark Sellke may publish in the future.
Co-authors
The 22 scholars most cited alongside Mark Sellke, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 23 | |
| 2 | 2022 | 14 | |
| 3 | 2020 | 13 | |
| 4 | 2018 | 11 | |
| 5 | 2023 | 10 | |
| 6 | 2022 | 10 | |
| 7 | 2019 | 9 | |
| 8 | 2024 | 8 | |
| 9 | 2024 | 7 | |
| 10 | 2014 | 6 | |
| 11 | 2021 | 5 | |
| 12 | 2012 | 4 | |
| 13 | 2023 | 4 | |
| 14 | 2022 | 4 | |
| 15 | 2025 | 3 | |
| 16 | 2024 | 3 | |
| 17 | 2021 | 3 | |
| 18 | 2024 | 2 | |
| 19 | 2018 | 2 | |
| 20 | Sample Complexity of Incentivized Exploration. | 2020 | 1 |
About Mark Sellke
Mark Sellke is a scholar working on Condensed Matter Physics, Artificial Intelligence, Statistics and Probability, Computational Theory and Mathematics and Mathematical Physics, having authored 27 papers that have together received 144 indexed citations. Recurring topics across this work include Theoretical and Computational Physics (10 papers), Markov Chains and Monte Carlo Methods (6 papers), Topological and Geometric Data Analysis (5 papers), Random Matrices and Applications (5 papers), Machine Learning and Algorithms (4 papers), Advanced Bandit Algorithms Research (4 papers), Control Systems and Identification (3 papers) and Stochastic processes and statistical mechanics (3 papers). The work is most often cited by research in Statistics and Probability (26 citations), Computer Graphics and Computer-Aided Design (11 citations), Condensed Matter Physics (35 citations), Mathematical Physics (20 citations) and Computational Theory and Mathematics (22 citations). Mark Sellke has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include A. El Alaoui, Andrea Montanari, Aleksandrs Slivkins, Victoria Kostina, Gireeja Ranade, Yuval Peres, Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li and Jerry Li. Their work appears in journals such as Communications in Mathematical Physics, Communications on Pure and Applied Mathematics, Journal of Statistical Physics, Operations Research and The Annals of Probability.
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.