Keith D. Levin

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	Toate	Din 2020
Referințe bibliografice	1207	852
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Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins UniversityAdresă de e-mail confirmată pe jhu.edu
Vince LyzinskiUniversity of MarylandAdresă de e-mail confirmată pe umd.edu
Minh TangNorth Carolina State UniversityAdresă de e-mail confirmată pe ncsu.edu
Avanti AthreyaApplied Mathematics and Statistics, Johns Hopkins UniversityAdresă de e-mail confirmată pe jhu.edu
Elizaveta (Liza) LevinaProfessor of Statistics, University of MichiganAdresă de e-mail confirmată pe umich.edu
Vladimir BravermanProfessor of Computer Science, Johns Hopkins University; Google Research; Adjunct Professor, Rice U.Adresă de e-mail confirmată pe cs.jhu.edu
Joshua T. VogelsteinDept of Biomedical Engineering, Johns Hopkins UniversityAdresă de e-mail confirmată pe jhu.edu
Karen LivescuTTI-ChicagoAdresă de e-mail confirmată pe ttic.edu
Aren JansenGoogle DeepMindAdresă de e-mail confirmată pe google.com
Katharine HenryData Scientist at Janssen R&D | PhD Computer Science from Johns Hopkins UniversityAdresă de e-mail confirmată pe jhu.edu
Herman KamperStellenbosch UniversityAdresă de e-mail confirmată pe sun.ac.za
Shane SettlePhD Student, Toyota Technological Institute at ChicagoAdresă de e-mail confirmată pe ttic.edu
Benjamin Van DurmeJohns Hopkins University / MicrosoftAdresă de e-mail confirmată pe jhu.edu
Fred RoostaUniversity of QueenslandAdresă de e-mail confirmată pe uq.edu.au
Michael MahoneyProfessor of Statistics, UC BerkeleyAdresă de e-mail confirmată pe stat.berkeley.edu
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Keith D. Levin

Assistant Professor, University of Wisconsin-Madison

Adresă de e-mail confirmată pe wisc.edu - Pagina de pornire

Network Analysis Pattern Recognition


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Statistical inference on random dot product graphs: a survey A Athreya, DE Fishkind, M Tang, CE Priebe, Y Park, JT Vogelstein, ... Journal of Machine Learning Research 18 (226), 1-92, 2018	307	2018
Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings K Levin, K Henry, A Jansen, K Livescu 2013 IEEE workshop on automatic speech recognition and understanding, 410-415, 2013	144	2013
A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition A Jansen, E Dupoux, S Goldwater, M Johnson, S Khudanpur, K Church, ... 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013	122	2013
Query-by-example search with discriminative neural acoustic word embeddings S Settle, K Levin, H Kamper, K Livescu arXiv preprint arXiv:1706.03818, 2017	102	2017
Segmental acoustic indexing for zero resource keyword search K Levin, A Jansen, B Van Durme 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015	83	2015
A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference K Levin, A Athreya, M Tang, V Lyzinski, Y Park, CE Priebe arXiv preprint arXiv:1705.09355, 2017	66*	2017
Bootstrapping networks with latent space structure K Levin, E Levina arXiv preprint arXiv:1907.10821, 2019	52	2019
Estimating a network from multiple noisy realizations CM Le, K Levin, E Levina	49	2018
A central limit theorem for an omnibus embedding of multiple random dot product graphs K Levin, A Athreya, M Tang, V Lyzinski, CE Priebe 2017 IEEE international conference on data mining workshops (ICDMW), 964-967, 2017	48	2017
Clustering problems on sliding windows V Braverman, H Lang, K Levin, M Monemizadeh Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete …, 2016	48	2016
Connectal coding: discovering the structures linking cognitive phenotypes to individual histories JT Vogelstein, EW Bridgeford, BD Pedigo, J Chung, K Levin, B Mensh, ... Current opinion in neurobiology 55, 199-212, 2019	24	2019
Out-of-sample extension of graph adjacency spectral embedding K Levin, F Roosta, M Mahoney, C Priebe International Conference on Machine Learning, 2975-2984, 2018	21	2018
On the consistency of the likelihood maximization vertex nomination scheme: Bridging the gap between maximum likelihood estimation and graph matching V Lyzinski, K Levin, DE Fishkind, CE Priebe Journal of Machine Learning Research 17 (179), 1-34, 2016	20	2016
On consistent vertex nomination schemes V Lyzinski, K Levin, CE Priebe Journal of Machine Learning Research 20 (69), 1-39, 2019	18	2019
Clustering on sliding windows in polylogarithmic space V Braverman, H Lang, K Levin, M Monemizadeh 35th IARCS Annual Conference on Foundations of Software Technology and …, 2015	16	2015
Vertex nomination: The canonical sampling and the extended spectral nomination schemes J Yoder, L Chen, H Pao, E Bridgeford, K Levin, DE Fishkind, C Priebe, ... Computational Statistics & Data Analysis 145, 106916, 2020	15	2020
Laplacian eigenmaps from sparse, noisy similarity measurements K Levin, V Lyzinski IEEE Transactions on Signal Processing 65 (8), 1988-2003, 2016	14	2016
Recovering low-rank structure from multiple networks with unknown edge distributions. K Levin, A Lodhia, E Levina Journal of machine learning research 23 (3), 2022	13	2022
Recovering shared structure from multiple networks with unknown edge distributions K Levin, A Lodhia, E Levina Journal of machine learning research 23 (3), 1-48, 2022	8	2022
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings K Levin, F Roosta, M Tang, MW Mahoney, CE Priebe Journal of Machine Learning Research 22 (194), 1-59, 2021	8	2021

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