| 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 | 277 | 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 | 141 | 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 | 97 | 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 | 60* | 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 |
| Estimating a network from multiple noisy realizations CM Le, K Levin, E Levina | 46 | 2018 |
| Bootstrapping networks with latent space structure K Levin, E Levina arXiv preprint arXiv:1907.10821, 2019 | 45 | 2019 |
| 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 | 43 | 2017 |
| 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 | 23 | 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 | 22 | 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 |
| 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 | 18 | 2022 |
| On consistent vertex nomination schemes V Lyzinski, K Levin, CE Priebe Journal of Machine Learning Research 20 (69), 1-39, 2019 | 18 | 2019 |
| 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 | 15 | 2016 |
| 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 | 15 | 2015 |
| 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 | 9 | 2021 |
| Comment: Ridge regression and regularization of large matrices CM Le, K Levin, PJ Bickel, E Levina Technometrics 62 (4), 443-446, 2020 | 5 | 2020 |
Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins UniversityVerified email at jhu.edu
