| Approaching optimality for solving SDD linear systems I Koutis, GL Miller, R Peng SIAM Journal on Computing 43 (1), 337-354, 2014 | 240 | 2014 |
| A nearly-m log n time solver for SDD linear systems I Koutis, GL Miller, R Peng Foundations of Computer Science (FOCS), 2011 IEEE 52nd Annual Symposium on …, 2011 | 214* | 2011 |
| Solving sdd linear systems in nearly m log 1/2 n time MB Cohen, R Kyng, GL Miller, JW Pachocki, R Peng, AB Rao, SC Xu Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 122* | 2014 |
| Efficient triangle counting in large graphs via degree-based vertex partitioning MN Kolountzakis, GL Miller, R Peng, CE Tsourakakis Internet Mathematics 8 (1-2), 161-185, 2012 | 116 | 2012 |
| 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 | 109 | 2015 |
| An efficient parallel solver for SDD linear systems R Peng, DA Spielman Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 91 | 2014 |
| Fully dynamic (1+ e)-approximate matchings M Gupta, R Peng 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 548-557, 2013 | 85 | 2013 |
| 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 | 71* | 2016 |
| Iterative row sampling M Li, GL Miller, R Peng 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 127-136, 2013 | 65* | 2013 |
| Faster spectral sparsification and numerical algorithms for SDD matrices I Koutis, A Levin, R Peng arXiv preprint arXiv:1209.5821, 2012 | 61* | 2012 |
| Parallel graph decompositions using random shifts GL Miller, R Peng, SC Xu Proceedings of the twenty-fifth annual ACM symposium on Parallelism in …, 2013 | 56 | 2013 |
| Approximate Undirected Maximum Flows in O(mpolylog(n)) Time R Peng Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete …, 2016 | 53* | 2016 |
| Linear-work greedy parallel approximate set cover and variants GE Blelloch, R Peng, K Tangwongsan Proceedings of the twenty-third annual ACM symposium on Parallelism in …, 2011 | 52 | 2011 |
| Partitioning well-clustered graphs: Spectral clustering works! R Peng, H Sun, L Zanetti Conference on Learning Theory, 1423-1455, 2015 | 47* | 2015 |
| Nearly-linear work parallel SDD solvers, low-diameter decomposition, and low-stretch subgraphs GE Blelloch, A Gupta, I Koutis, GL Miller, R Peng, K Tangwongsan Theory of Computing Systems 55 (3), 521-554, 2014 | 46 | 2014 |
| Scalable large near-clique detection in large-scale networks via sampling M Mitzenmacher, J Pachocki, R Peng, C Tsourakakis, SC Xu Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 45 | 2015 |
| Almost-linear-time algorithms for markov chains and new spectral primitives for directed graphs MB Cohen, J Kelner, J Peebles, R Peng, AB Rao, A Sidford, A Vladu Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 42 | 2017 |
| A fast solver for a class of linear systems I Koutis, GL Miller, R Peng Communications of the ACM 55 (10), 99-107, 2012 | 41* | 2012 |
| On fully dynamic graph sparsifiers I Abraham, D Durfee, I Koutis, S Krinninger, R Peng 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016 | 38 | 2016 |
| Runtime Guarantees for Regression Problems HH Chin, A Madry, G Miller, R Peng | 38 | 2011 |
Gary L. MillerCarnegie Mellon UniversityVerified email at cs.cmu.edu
