| 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 | 226 | 2015 |
| Randomized block krylov methods for stronger and faster approximate singular value decomposition C Musco, C Musco Advances in Neural Information Processing Systems, 1396-1404, 2015 | 144* | 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 | 139 | 2015 |
| 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 | 112 | 2014 |
| Recursive Sampling for the Nyström Method C Musco, C Musco arXiv preprint arXiv:1605.07583, 2016 | 94 | 2016 |
| 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 | 90* | 2016 |
| 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 | 78* | 2017 |
| 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 | 62 | 2017 |
| Online row sampling MB Cohen, C Musco, J Pachocki arXiv preprint arXiv:1604.05448, 2016 | 36 | 2016 |
| 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 | 29 | 2017 |
| Computational tradeoffs in biological neural networks: Self-stabilizing winner-take-all networks N Lynch, C Musco, M Parter arXiv preprint arXiv:1610.02084, 2016 | 27 | 2016 |
| Ant-inspired density estimation via random walks C Musco, HH Su, NA Lynch Proceedings of the National Academy of Sciences 114 (40), 10534-10541, 2017 | 26 | 2017 |
| Minimizing polarization and disagreement in social networks C Musco, C Musco, CE Tsourakakis Proceedings of the 2018 World Wide Web Conference, 369-378, 2018 | 25 | 2018 |
| Principal component projection without principal component analysis R Frostig, C Musco, C Musco, A Sidford International Conference on Machine Learning, 2349-2357, 2016 | 22 | 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 | 16 | 2019 |
| 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 | 14 | 2018 |
| Neuro-ram unit with applications to similarity testing and compression in spiking neural networks N Lynch, C Musco, M Parter arXiv preprint arXiv:1706.01382, 2017 | 14 | 2017 |
| 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 | 14 | 2017 |
| Distributed house-hunting in ant colonies M Ghaffari, C Musco, T Radeva, N Lynch Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing …, 2015 | 13 | 2015 |
| Spiking neural networks: An algorithmic perspective N Lynch, C Musco, M Parter 5th Workshop on Biological Distributed Algorithms (BDA 2017), 2017 | 11 | 2017 |
Christopher MuscoAssistant Professor, New York UniversityVerified email at nyu.edu
