Jonathan Niles‐Weed

880 total citations
17 papers, 175 citations indexed

About

Jonathan Niles‐Weed is a scholar working on Applied Mathematics, Statistics and Probability and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jonathan Niles‐Weed has authored 17 papers receiving a total of 175 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Applied Mathematics, 6 papers in Statistics and Probability and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jonathan Niles‐Weed's work include Point processes and geometric inequalities (7 papers), Geometric Analysis and Curvature Flows (4 papers) and Random Matrices and Applications (3 papers). Jonathan Niles‐Weed is often cited by papers focused on Point processes and geometric inequalities (7 papers), Geometric Analysis and Curvature Flows (4 papers) and Random Matrices and Applications (3 papers). Jonathan Niles‐Weed collaborates with scholars based in United States, France and Switzerland. Jonathan Niles‐Weed's co-authors include Philippe Rigollet, Jason M. Altschuler, Quentin Berthet, Larry Wasserman, Eustasio del Barrio, Sivaraman Balakrishnan, Jean–Michel Loubes, Sheng Liu, Alexander S. Wein and Afonso S. Bandeira and has published in prestigious journals such as Scientific Reports, Science Advances and The Annals of Statistics.

In The Last Decade

Jonathan Niles‐Weed

16 papers receiving 167 citations

Peers

Jonathan Niles‐Weed

SPS

Yvik Swan Belgium

Nate Strawn United States

Pierre Calka France

Franca Hoffmann United States

T. K. Samanta India

Raphaël Lachièze-Rey France

Xiucai Ding United States

Weiming Li China

Bruno Schapira France

Yvik Swan Belgium

Jonathan Niles‐Weed

130 ×2.0

48 ×0.9

33 ×1.2

62 ×3.1

0 ×0.0

SPS

Citations per year, relative to Jonathan Niles‐Weed Jonathan Niles‐Weed (= 1×) peers Yvik Swan

Countries citing papers authored by Jonathan Niles‐Weed

Since Specialization

Citations

This map shows the geographic impact of Jonathan Niles‐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 Niles‐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 Niles‐Weed more than expected).

Fields of papers citing papers by Jonathan Niles‐Weed

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan Niles‐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 Niles‐Weed. The network helps show where Jonathan Niles‐Weed may publish in the future.

Co-authorship network of co-authors of Jonathan Niles‐Weed

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Niles‐Weed. A scholar is included among the top collaborators of Jonathan Niles‐Weed 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 Jonathan Niles‐Weed. Jonathan Niles‐Weed is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Bryan, Jordan, Jonathan Niles‐Weed, & Peter D. Hoff. (2025). The multirank likelihood for semiparametric canonical correlation analysis. Journal of Multivariate Analysis. 210. 105484–105484.

Niles‐Weed, Jonathan, et al.. (2025). Optimal transport map estimation in general function spaces. The Annals of Statistics. 53(3). 1 indexed citations

Niles‐Weed, Jonathan, et al.. (2025). Statistical Optimal Transport. Lecture notes in mathematics. 2 indexed citations

Niles‐Weed, Jonathan, et al.. (2024). Sharp convergence rates for empirical optimal transport with smooth costs. The Annals of Applied Probability. 34(1B). 12 indexed citations

Balakrishnan, Sivaraman, et al.. (2024). Plugin estimation of smooth optimal transport maps. The Annals of Statistics. 52(3). 16 indexed citations

Bandeira, Afonso S., et al.. (2023). Estimation under group actions: Recovering orbits from invariants. Applied and Computational Harmonic Analysis. 66. 236–319. 17 indexed citations

Carleton, W. Christopher, et al.. (2023). Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor. Scientific Reports. 13(1). 17913–17913. 4 indexed citations

Barrio, Eustasio del, et al.. (2023). An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs. SIAM Journal on Mathematics of Data Science. 5(3). 639–669. 15 indexed citations

Niles‐Weed, Jonathan & Quentin Berthet. (2022). Minimax estimation of smooth densities in Wasserstein distance. The Annals of Statistics. 50(3). 9 indexed citations

10.

Altschuler, Jason M., et al.. (2022). Asymptotics for Semidiscrete Entropic Optimal Transport. SIAM Journal on Mathematical Analysis. 54(2). 1718–1741. 17 indexed citations

11.

Niles‐Weed, Jonathan & Philippe Rigollet. (2022). Estimation of Wasserstein distances in the Spiked Transport Model. Bernoulli. 28(4). 26 indexed citations

12.

Evans, Damian, Scott G. Ortman, Miriam T. Stark, et al.. (2021). Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex. Science Advances. 7(19). 21 indexed citations

13.

Niles‐Weed, Jonathan, et al.. (2021). Asymptotics of Smoothed Wasserstein Distances. Potential Analysis. 56(4). 571–595. 9 indexed citations

14.

Niles‐Weed, Jonathan, et al.. (2021). Dimension-free log-Sobolev inequalities for mixture distributions. Journal of Functional Analysis. 281(11). 109236–109236. 5 indexed citations

15.

Huang, De, Jonathan Niles‐Weed, Joel A. Tropp, & Rachel Ward. (2021). Matrix Concentration for Products. Foundations of Computational Mathematics. 22(6). 1767–1799. 7 indexed citations

16.

Liu, Sheng, Jonathan Niles‐Weed, Narges Razavian, & Carlos Fernandez‐Granda. (2020). Early-Learning Regularization Prevents Memorization of Noisy Labels. Neural Information Processing Systems. 33. 20331–20342. 13 indexed citations

17.

Niles‐Weed, Jonathan, et al.. (2020). The All-or-Nothing Phenomenon in Sparse Tensor PCA. Neural Information Processing Systems. 33. 17674–17684. 1 indexed citations

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.

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