| Maximum flow and minimum-cost flow in almost-linear time L Chen, R Kyng, YP Liu, R Peng, MP Gutenberg, S Sachdeva FOCS 2022. (IEEE 63rd Annual Symposium on Foundations of Computer Science …, 2022 | 338 | 2022 |
| Approximate Gaussian Elimination for Laplacians-fast, sparse, and simple R Kyng, S Sachdeva FOCS 2016. (IEEE 57th Annual Symposium on Foundations of Computer Science …, 2016 | 223 | 2016 |
| Sparsified cholesky and multigrid solvers for connection laplacians R Kyng, YT Lee, R Peng, S Sachdeva, DA Spielman STOC 2016. (Proceedings of the forty-eighth annual ACM symposium on Theory …, 2016 | 159 | 2016 |
| Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model G Zhang, L Li, Z Nado, J Martens, S Sachdeva, G Dahl, C Shallue, ... (NeurIPS 2019) Advances in Neural Information Processing Systems, 8196-8207, 2019 | 157 | 2019 |
| Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator L Orecchia, S Sachdeva, NK Vishnoi STOC 2012 (Proceedings of the 44th symposium on Theory of Computing), 1141-1160, 2012 | 131 | 2012 |
| Faster Algorithms via Approximation Theory S Sachdeva, NK Vishnoi Foundations and Trends® in Theoretical Computer Science 9 (2), 125-210, 2014 | 122 | 2014 |
| Provable ICA with unknown Gaussian noise, and implications for Gaussian mixtures and autoencoders S Arora, R Ge, A Moitra, S Sachdeva Algorithmica 72 (1), 215-236, 2015 | 102 | 2015 |
| Algorithms for Lipschitz learning on graphs R Kyng, A Rao, S Sachdeva, DA Spielman Proceedings of The 28th Conference on Learning Theory, 1190-1223, 2015 | 100 | 2015 |
| Sampling random spanning trees faster than matrix multiplication D Durfee, R Kyng, J Peebles, AB Rao, S Sachdeva Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 88 | 2017 |
| Graph sparsification, spectral sketches, and faster resistance computation via short cycle decompositions T Chu, Y Gao, R Peng, S Sachdeva, S Sawlani, J Wang SIAM Journal on Computing 52 (6), FOCS18-85-FOCS18-157, 2020 | 81 | 2020 |
| Iterative Refinement for ℓp-norm Regression D Adil, R Kyng, R Peng, S Sachdeva Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 77 | 2019 |
| Finding overlapping communities in social networks: Toward a rigorous approach S Arora, R Ge, S Sachdeva, G Schoenebeck Proceedings of the 13th ACM Conference on Electronic Commerce, 37-54, 2012 | 67 | 2012 |
| Fast, Provable Algorithms for Isotonic Regression in all L_p-norms R Kyng, A Rao, S Sachdeva Advances in Neural Information Processing Systems, 2701-2709, 2015 | 66 | 2015 |
| A deterministic almost-linear time algorithm for minimum-cost flow J van den Brand, L Chen, R Kyng, YP Liu, R Peng, MP Gutenberg, ... 64th IEEE Annual Symposium on Foundations of Computer Science, FOCS, 2023 | 48 | 2023 |
| Regularized linear autoencoders recover the principal components, eventually X Bao, J Lucas, S Sachdeva, RB Grosse Advances in Neural Information Processing Systems 33, 2020 | 47 | 2020 |
| Flows in almost linear time via adaptive preconditioning R Kyng, R Peng, S Sachdeva, D Wang Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 45 | 2019 |
| Fast, provably convergent IRLS algorithm for p-norm linear regression D Adil, R Peng, S Sachdeva Advances in Neural Information Processing Systems, 14189-14200, 2019 | 43 | 2019 |
| Optimal inapproximability for scheduling problems via structural hardness for hypergraph vertex cover S Sachdeva, R Saket Computational Complexity (CCC), 2013 IEEE Conference on, 219-229, 2013 | 40 | 2013 |
| A framework for analyzing resparsification algorithms R Kyng, J Pachocki, R Peng, S Sachdeva Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 38 | 2017 |
| Electron-proton dynamics in deep learning Q Zhang, R Panigrahy, S Sachdeva, A Rahimi arXiv preprint arXiv:1702.00458, 1-31, 2017 | 34 | 2017 |
Rasmus KyngETH ZurichGeverifieerd e-mailadres voor kyng.dk