| Fast Estimation of via Stochastic Lanczos Quadrature S Ubaru, J Chen, Y Saad SIAM Journal on Matrix Analysis and Applications 38 (4), 1075-1099, 2017 | 210 | 2017 |
| Fast methods for estimating the numerical rank of large matrices S Ubaru, Y Saad International Conference on Machine Learning, 468-477, 2016 | 68 | 2016 |
| 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 | 43 | 2017 |
| Dynamic graph convolutional networks using the tensor M-product OA Malik, S Ubaru, L Horesh, ME Kilmer, H Avron Proceedings of the 2021 SIAM international conference on data mining (SDM …, 2021 | 39 | 2021 |
| Applications of trace estimation techniques S Ubaru, Y Saad International Conference on High Performance Computing in Science and …, 2017 | 36 | 2017 |
| Formation enthalpies for transition metal alloys using machine learning S Ubaru, A Międlar, Y Saad, JR Chelikowsky Physical Review B 95 (21), 214102, 2017 | 34 | 2017 |
| Quantum topological data analysis with linear depth and exponential speedup S Ubaru, IY Akhalwaya, MS Squillante, KL Clarkson, L Horesh arXiv preprint arXiv:2108.02811, 2021 | 30 | 2021 |
| Topological data analysis on noisy quantum computers IY Akhalwaya, S Ubaru, KL Clarkson, MS Squillante, V Jejjala, YH He, ... The Twelfth International Conference on Learning Representations, 2024 | 28* | 2024 |
| Efficient scaling of dynamic graph neural networks VT Chakaravarthy, SS Pandian, S Raje, Y Sabharwal, T Suzumura, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 28 | 2021 |
| Multilabel classification with group testing and codes S Ubaru, A Mazumdar International Conference on Machine Learning, 3492-3501, 2017 | 28 | 2017 |
| Fast estimation of approximate matrix ranks using spectral densities S Ubaru, Y Saad, AK Seghouane Neural computation 29 (5), 1317-1351, 2017 | 25 | 2017 |
| Union of intersections (uoi) for interpretable data driven discovery and prediction K Bouchard, A Bujan, F Roosta, S Ubaru, M Prabhat, A Snijders, JH Mao, ... Advances in Neural Information Processing Systems 30, 2017 | 25 | 2017 |
| Improving the incoherence of a learned dictionary via rank shrinkage S Ubaru, AK Seghouane, Y Saad Neural computation 29 (1), 263-285, 2016 | 25 | 2016 |
| Analysis of stochastic Lanczos quadrature for spectrum approximation T Chen, T Trogdon, S Ubaru International Conference on Machine Learning, 1728-1739, 2021 | 20 | 2021 |
| Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription S Ubaru, L Horesh, G Cohen Journal of biomedical informatics 122, 103901, 2021 | 19* | 2021 |
| Low rank approximation and decomposition of large matrices using error correcting codes S Ubaru, A Mazumdar, Y Saad IEEE Transactions on Information Theory 63 (9), 5544-5558, 2017 | 16 | 2017 |
| Low rank approximation using error correcting coding matrices S Ubaru, A Mazumdar, Y Saad International Conference on Machine Learning, 702-710, 2015 | 14 | 2015 |
| Quantum Graph Transformers G Kollias, V Kalantzis, T Salonidis, S Ubaru ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 13 | 2023 |
| Capacity analysis of vector symbolic architectures KL Clarkson, S Ubaru, E Yang arXiv preprint arXiv:2301.10352, 2023 | 13 | 2023 |
| Randomized matrix-free quadrature for spectrum and spectral sum approximation T Chen, T Trogdon, S Ubaru arXiv preprint arXiv:2204.01941, 2022 | 13 | 2022 |
Lior HoreshIBM TJ Watson Research Center | Columbia University在 us.ibm.com 的电子邮件经过验证
