| Faster fundamental graph algorithms via learned predictions J Chen, S Silwal, A Vakilian, F Zhang International Conference on Machine Learning, 3583-3602, 2022 | 46 | 2022 |
| Adversarial robustness of streaming algorithms through importance sampling V Braverman, A Hassidim, Y Matias, M Schain, S Silwal, S Zhou Advances in Neural Information Processing Systems 34, 3544-3557, 2021 | 40 | 2021 |
| Learning-Augmented -means Clustering JC Ergun, Z Feng, S Silwal, DP Woodruff, S Zhou arXiv preprint arXiv:2110.14094, 2021 | 35 | 2021 |
| Learning-based support estimation in sublinear time T Eden, P Indyk, S Narayanan, R Rubinfeld, S Silwal, T Wagner arXiv preprint arXiv:2106.08396, 2021 | 31 | 2021 |
| Triangle and four cycle counting with predictions in graph streams JY Chen, T Eden, P Indyk, H Lin, S Narayanan, R Rubinfeld, S Silwal, ... arXiv preprint arXiv:2203.09572, 2022 | 24 | 2022 |
| The white-box adversarial data stream model M Ajtai, V Braverman, TS Jayram, S Silwal, A Sun, DP Woodruff, S Zhou Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2022 | 20 | 2022 |
| Exponentially improving the complexity of simulating the Weisfeiler-Lehman test with graph neural networks A Aamand, J Chen, P Indyk, S Narayanan, R Rubinfeld, N Schiefer, ... Advances in Neural Information Processing Systems 35, 27333-27346, 2022 | 17 | 2022 |
| Dimensionality reduction for wasserstein barycenter Z Izzo, S Silwal, S Zhou Advances in neural information processing systems 34, 15582-15594, 2021 | 16 | 2021 |
| Kwikbucks: Correlation clustering with cheap-weak and expensive-strong signals S Silwal, S Ahmadian, A Nystrom, A McCallum, D Ramachandran, ... Proceedings of The Fourth Workshop on Simple and Efficient Natural Language …, 2023 | 11 | 2023 |
| Optimal algorithms for linear algebra in the current matrix multiplication time Y Cherapanamjeri, S Silwal, DP Woodruff, S Zhou Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 11 | 2023 |
| Hardness and algorithms for robust and sparse optimization E Price, S Silwal, S Zhou International Conference on Machine Learning, 17926-17944, 2022 | 9 | 2022 |
| Robust algorithms on adaptive inputs from bounded adversaries Y Cherapanamjeri, S Silwal, DP Woodruff, F Zhang, Q Zhang, S Zhou arXiv preprint arXiv:2304.07413, 2023 | 8 | 2023 |
| Directed random geometric graphs J Michel, S Reddy, R Shah, S Silwal, R Movassagh Journal of Complex Networks 7 (5), 792-816, 2019 | 8 | 2019 |
| Improved space bounds for learning with experts A Aamand, JY Chen, HL Nguyen, S Silwal arXiv preprint arXiv:2303.01453, 2023 | 7 | 2023 |
| Sub-quadratic algorithms for kernel matrices via kernel density estimation A Bakshi, P Indyk, P Kacham, S Silwal, S Zhou arXiv preprint arXiv:2212.00642, 2022 | 7 | 2022 |
| Randomized dimensionality reduction for facility location and single-linkage clustering S Narayanan, S Silwal, P Indyk, O Zamir International Conference on Machine Learning, 7948-7957, 2021 | 7 | 2021 |
| Learning-augmented algorithms for online linear and semidefinite programming E Grigorescu, YS Lin, S Silwal, M Song, S Zhou Advances in Neural Information Processing Systems 35, 38643-38654, 2022 | 6 | 2022 |
| A note on the universality of ESDs of inhomogeneous random matrices V Jain, S Silwal arXiv preprint arXiv:2006.05418, 2020 | 6 | 2020 |
| Using dimensionality reduction to optimize t-sne R Shah, S Silwal arXiv preprint arXiv:1912.01098, 2019 | 6 | 2019 |
| Property testing of LP-type problems R Epstein, S Silwal arXiv preprint arXiv:1911.08320, 2019 | 6 | 2019 |
Samson ZhouTexas A&M UniversityVerified email at alum.mit.edu
