Jason M. Klusowski
- Artificial Intelligence
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Computational Mechanics
- Statistics and Probability
- Topics
- Statistical Methods and Inference (9 papers)Sparse and Compressive Sensing Techniques (3 papers)Machine Learning and Data Classification (3 papers)
- Journals
- Journal of the American Statistical AssociationIEEE Transactions on Information TheoryJournal of the Royal Statistical Society Series B (Statistical Methodology)
- Partner nations
- United StatesUnited KingdomFrance
In The Last Decade
Jason M. Klusowski
10 papers receiving 130 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 81
- Statistical and Nonlinear Physics 40
- Computer Vision and Pattern Recognition 28
- Computational Mechanics 27
- Statistics and Probability 19
Countries citing papers authored by Jason M. Klusowski
This map shows the geographic impact of Jason M. Klusowski'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 Jason M. Klusowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason M. Klusowski more than expected).
Fields of papers citing papers by Jason M. Klusowski
This network shows the impact of papers produced by Jason M. Klusowski. 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 Jason M. Klusowski. The network helps show where Jason M. Klusowski may publish in the future.
Co-authorship network of co-authors of Jason M. Klusowski
This figure shows the co-authorship network connecting the top 25 collaborators of Jason M. Klusowski. A scholar is included among the top collaborators of Jason M. Klusowski 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 Jason M. Klusowski. Jason M. Klusowski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 20 | |
| 6 | Sharp Analysis of a Simple Model for Random Forests | 0 |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 6 | |
| 10 | 7 | |
| 11 | Complete Analysis of a Random Forest Model | 7 |
| 12 | 0 | |
| 13 | 64 | |
| 14 | 12 | |
| 15 | Uniform Approximation by Neural Networks Activated by First and Second Order Ridge Splines | 7 |
About Jason M. Klusowski
Jason M. Klusowski is a scholar working on Statistics and Probability, Discrete Mathematics and Combinatorics and Artificial Intelligence, having authored 15 papers that have together received 131 indexed citations. Recurring topics across this work include Statistical Methods and Inference (9 papers), Sparse and Compressive Sensing Techniques (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (40 citations), Artificial Intelligence (81 citations) and Statistics and Probability (19 citations). Jason M. Klusowski has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include Andrew R. Barron, Yihong Wu, Cynthia Rush, Weijie Su, Matias D. Cattaneo and Jonathan W. Siegel. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Information Theory and Journal of the Royal Statistical Society Series B (Statistical Methodology).
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.