| Dimensionality reduction for k-means clustering and low rank approximation MB Cohen, S Elder, C Musco, C Musco, M Persu Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 446 | 2015 |
| Randomized block krylov methods for stronger and faster approximate singular value decomposition C Musco, C Musco Advances in neural information processing systems 28, 2015 | 365 | 2015 |
| Uniform sampling for matrix approximation MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford Proceedings of the 2015 conference on innovations in theoretical computer …, 2015 | 261 | 2015 |
| Recursive Sampling for the Nyström Method C Musco, C Musco arXiv preprint arXiv:1605.07583, 2016 | 251 | 2016 |
| Random Fourier features for kernel ridge regression: Approximation bounds and statistical guarantees H Avron, M Kapralov, C Musco, C Musco, A Velingker, A Zandieh International conference on machine learning, 253-262, 2017 | 195 | 2017 |
| Input sparsity time low-rank approximation via ridge leverage score sampling MB Cohen, C Musco, C Musco Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 185* | 2017 |
| Single pass spectral sparsification in dynamic streams M Kapralov, YT Lee, C Musco, C Musco, A Sidford Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on …, 2014 | 175 | 2014 |
| Minimizing polarization and disagreement in social networks C Musco, C Musco, CE Tsourakakis Proceedings of the 2018 world wide web conference, 369-378, 2018 | 172 | 2018 |
| Hutch++: Optimal stochastic trace estimation RA Meyer, C Musco, C Musco, DP Woodruff Symposium on Simplicity in Algorithms (SOSA), 142-155, 2021 | 130 | 2021 |
| Faster eigenvector computation via shift-and-invert preconditioning D Garber, E Hazan, C Jin, C Musco, P Netrapalli, A Sidford International Conference on Machine Learning, 2626-2634, 2016 | 128* | 2016 |
| Online row sampling MB Cohen, C Musco, J Pachocki Theory of Computing 16 (1), 1-25, 2020 | 79* | 2020 |
| Near optimal linear algebra in the online and sliding window models SZ Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco ... arXiv preprint arXiv:1805.03765, 2018 | 69* | 2018 |
| Stability of the Lanczos method for matrix function approximation C Musco, C Musco, A Sidford Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 64 | 2018 |
| Sublinear time low-rank approximation of positive semidefinite matrices C Musco, DP Woodruff 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017 | 63 | 2017 |
| Node embeddings and exact low-rank representations of complex networks S Chanpuriya, C Musco, K Sotiropoulos, C Tsourakakis Advances in neural information processing systems 33, 13185-13198, 2020 | 47 | 2020 |
| Infinitewalk: Deep network embeddings as laplacian embeddings with a nonlinearity S Chanpuriya, C Musco Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 45 | 2020 |
| Ant-inspired density estimation via random walks C Musco, HH Su, N Lynch Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing …, 2016 | 45 | 2016 |
| A universal sampling method for reconstructing signals with simple fourier transforms H Avron, M Kapralov, C Musco, C Musco, A Velingker, A Zandieh Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 42 | 2019 |
| Spectrum approximation beyond fast matrix multiplication: Algorithms and hardness C Musco, P Netrapalli, A Sidford, S Ubaru, DP Woodruff arXiv preprint arXiv:1704.04163, 2017 | 41 | 2017 |
| Coresets for classification–simplified and strengthened T Mai, C Musco, A Rao Advances in Neural Information Processing Systems 34, 11643-11654, 2021 | 38 | 2021 |
Christopher MuscoAssistant Professor, New York University確認したメール アドレス: nyu.edu
