Jonathan Weed
Impact in
- Space and Planetary Science top 10%
- Archaeological Research and Protection
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- Markov Chains and Monte Carlo Methods
Papers in
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- Image Processing and 3D Reconstruction 1
- Co-authors
- Philippe Rigollet (3 shared papers)Jason M. Altschuler (1 shared paper)Quentin Berthet (1 shared paper)Damian Evans (1 shared paper)Francis Bach (1 shared paper)Vianney Perchet (1 shared paper)Geoffrey Schiebinger (1 shared paper)Mor Nitzan (1 shared paper)
- Journals
- Bernoulli (1 paper)Journal of Machine Learning Research (1 paper)PLoS ONE (1 paper)International Journal of Pure and Apllied Mathematics (1 paper)Oxford University Research Archive (ORA) (University of Oxford) (1 paper)
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Jonathan Weed
9 papers receiving 88 citations
Peers
Comparison fields: 5 of 52
- Space and Planetary Science 13
- Statistics and Probability 20
- Geology 8
- Applied Mathematics 15
- Paleontology 8
Countries citing papers authored by Jonathan Weed
This map shows the geographic impact of Jonathan Weed'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 Jonathan Weed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Weed more than expected).
Fields of papers citing papers by Jonathan Weed
This network shows the impact of papers produced by Jonathan Weed. 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 Jonathan Weed. The network helps show where Jonathan Weed may publish in the future.
Co-authors
The 12 scholars most cited alongside Jonathan Weed, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration | 2017 | 28 |
| 2 | 2018 | 24 | |
| 3 | Estimation of smooth densities in Wasserstein distance | 2019 | 16 |
| 4 | Online learning in repeated auctions | 2016 | 7 |
| 5 | 2019 | 7 | |
| 6 | 2019 | 4 | |
| 7 | Statistical optimal transport via factored couplings | 2019 | 3 |
| 8 | 2005 | 3 | |
| 9 | 2015 | 2 | |
| 10 | 2005 | 0 |
About Jonathan Weed
Jonathan Weed is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Applied Mathematics, having authored 10 papers that have together received 94 indexed citations. Recurring topics across this work include Geometric Analysis and Curvature Flows (2 papers), Statistical Methods and Inference (2 papers), Advanced Antenna and Metasurface Technologies (2 papers), Single-cell and spatial transcriptomics (1 paper), Data Stream Mining Techniques (1 paper), Data Management and Algorithms (1 paper), Image Processing and 3D Reconstruction (1 paper) and Polynomial and algebraic computation (1 paper). The work is most often cited by research in Space and Planetary Science (13 citations), Statistics and Probability (20 citations), Geology (8 citations), Applied Mathematics (15 citations) and Paleontology (8 citations). Jonathan Weed has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Philippe Rigollet, Jason M. Altschuler, Quentin Berthet, Damian Evans, Francis Bach, Vianney Perchet, Geoffrey Schiebinger, Mor Nitzan, Ziv Goldfeld and Kristjan Greenewald. Their work appears in journals such as Bernoulli, Journal of Machine Learning Research, PLoS ONE, International Journal of Pure and Apllied Mathematics and Oxford University Research Archive (ORA) (University of Oxford).
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.