Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Avanti Athreya"'
Autor:
Tianyi Chen, Youngser Park, Ali Saad-Eldin, Zachary Lubberts, Avanti Athreya, Benjamin D. Pedigo, Joshua T. Vogelstein, Francesca Puppo, Gabriel A. Silva, Alysson R. Muotri, Weiwei Yang, Christopher M. White, Carey E. Priebe
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-13 (2023)
Abstract Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring
Externí odkaz:
https://doaj.org/article/dd431d1a9d754a39a8e3c7919c7480ff
Autor:
Donniell E. Fishkind, Felix Parker, Hamilton Sawczuk, Lingyao Meng, Eric Bridgeford, Avanti Athreya, Carey Priebe, Vince Lyzinski
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-27 (2021)
Abstract The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matchi
Externí odkaz:
https://doaj.org/article/217e4cb8070b46ea848f268eed58bd88
Autor:
Avanti Athreya, Zachary Lubberts, Carey E. Priebe, Youngser Park, Minh Tang, Vince Lyzinski, Michael Kane, Bryan W. Lewis
Publikováno v:
Journal of Computational and Graphical Statistics. 32:145-156
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b887df2a069e3d15a7f0237e72b38be
https://doi.org/10.1080/10618600.2022.2082972
https://doi.org/10.1080/10618600.2022.2082972
Publikováno v:
Sankhya A. 84:36-63
We derive the limiting distribution for the outlier eigenvalues of the adjacency matrix for random graphs with independent edges whose edge probability matrices have low-rank structure. We show that when the number of vertices tends to infinity, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1329a26c12c0ea203b8fd7850d764a8c
https://doi.org/10.1007/s13171-021-00268-x
https://doi.org/10.1007/s13171-021-00268-x
Autor:
Jesús, Arroyo, Avanti, Athreya, Joshua, Cape, Guodong, Chen, Carey E, Priebe, Joshua T, Vogelstein
Publikováno v:
Journal of machine learning research : JMLR
The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-stud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::817824638694fa69fb89eec31d4ffa25
https://pubmed.ncbi.nlm.nih.gov/34650343
https://pubmed.ncbi.nlm.nih.gov/34650343
Publikováno v:
Statist. Sci. 36, no. 1 (2021), 68-88
We define a latent structure model (LSM) random graph as a random dot product graph (RDPG) in which the latent position distribution incorporates both probabilistic and geometric constraints, delineated by a family of underlying distributions on some
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a49730b50567db26d40b9e06fd939af
https://projecteuclid.org/euclid.ss/1608541219
https://projecteuclid.org/euclid.ss/1608541219
Autor:
Eric W. Bridgeford, Avanti Athreya, Vince Lyzinski, Hamilton Sawczuk, Felix Parker, Lingyao Meng, Carey E. Priebe, Donniell E. Fishkind
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-27 (2021)
The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matching. Unfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa4adf310a26e6eda818a681388d60a8
Publikováno v:
Electronic Journal of Statistics. 15
Inference on vertex-aligned graphs is of wide theoretical and practical importance.There are, however, few flexible and tractable statistical models for correlated graphs, and even fewer comprehensive approaches to parametric inference on data arisin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a80b902c0a26ab844589ea44ed8dadc
https://doi.org/10.1214/21-ejs1839
https://doi.org/10.1214/21-ejs1839
Autor:
Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Carey E. Priebe, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Marah Abdin, Piali Choudhury, Weiwei Yang, Christopher W. White
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8460bf599e6a7ee43241bb1f990a1a21
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity. Beyond mere adjacency matrices, many real networks also involve vertex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50306be76f574931e70017d3f8833fee