| Approximate Gaussian Elimination for Laplacians-fast, sparse, and simple R Kyng, S Sachdeva 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016 | 134 | 2016 |
| Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator L Orecchia, S Sachdeva, NK Vishnoi Proceedings of the 44th symposium on Theory of Computing, 1141-1160, 2012 | 109 | 2012 |
| Sparsified cholesky and multigrid solvers for connection laplacians R Kyng, YT Lee, R Peng, S Sachdeva, DA Spielman Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 103 | 2016 |
| 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 | 88 | 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 | 64 | 2015 |
| 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 | 62 | 2012 |
| Faster Algorithms via Approximation Theory S Sachdeva, NK Vishnoi Foundations and Trends® in Theoretical Computer Science 9 (2), 125-210, 2014 | 55 | 2014 |
| 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 | 54 | 2017 |
| Convergence Results for Neural Networks via Electrodynamics R Panigrahy, A Rahimi, S Sachdeva, Q Zhang 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2017 | 50* | 2017 |
| 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 | 47 | 2015 |
| 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, FOCS18-85-FOCS18-157, 2020 | 39 | 2020 |
| 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, ... Advances in Neural Information Processing Systems, 8196-8207, 2019 | 38 | 2019 |
| 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 | 35 | 2019 |
| 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 | 30 | 2017 |
| 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 | 27 | 2013 |
| 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 | 19 | 2019 |
| Faster p-norm minimizing flows, via smoothed q-norm problems D Adil, S Sachdeva Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 13 | 2020 |
| 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 | 13 | 2019 |
| Real analysis in computer science: A collection of open problems Y Filmus, H Hatami, S Heilman, E Mossel, R O’Donnell, S Sachdeva, ... Simons Institute, Berkeley, CA, compiled in, 2014 | 12 | 2014 |
| Inapproximability of minimum vertex cover on k-uniform k-partite hypergraphs V Guruswami, S Sachdeva, R Saket SIAM Journal on Discrete Mathematics 29 (1), 36-58, 2015 | 11 | 2015 |
Rasmus KyngETH ZurichVerified email at kyng.dk
