| Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors SB Hopkins, T Schramm, J Shi, D Steurer Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 156 | 2016 |
| The power of sum-of-squares for detecting hidden structures SB Hopkins, PK Kothari, A Potechin, P Raghavendra, T Schramm, ... 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017 | 151 | 2017 |
| Near optimal lp rounding algorithm for correlationclustering on complete and complete k-partite graphs S Chawla, K Makarychev, T Schramm, G Yaroslavtsev Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 136 | 2015 |
| High dimensional estimation via sum-of-squares proofs P Raghavendra, T Schramm, D Steurer Proceedings of the International Congress of Mathematicians: Rio de Janeiro …, 2018 | 72 | 2018 |
| Strongly refuting random csps below the spectral threshold P Raghavendra, S Rao, T Schramm Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 72 | 2017 |
| Statistical query algorithms and low-degree tests are almost equivalent M Brennan, G Bresler, SB Hopkins, J Li, T Schramm arXiv preprint arXiv:2009.06107, 2020 | 65 | 2020 |
| Computational barriers to estimation from low-degree polynomials T Schramm, AS Wein The Annals of Statistics 50 (3), 1833-1858, 2022 | 64 | 2022 |
| Computing exact minimum cuts without knowing the graph A Rubinstein, T Schramm, SM Weinberg arXiv preprint arXiv:1711.03165, 2017 | 61 | 2017 |
| Global and local information in clustering labeled block models V Kanade, E Mossel, T Schramm IEEE Transactions on Information Theory 62 (10), 5906-5917, 2016 | 57 | 2016 |
| Fast and robust tensor decomposition with applications to dictionary learning T Schramm, D Steurer Conference on Learning Theory, 1760-1793, 2017 | 54 | 2017 |
| On the integrality gap of degree-4 sum of squares for planted clique SB Hopkins, P Kothari, AH Potechin, P Raghavendra, T Schramm ACM Transactions on Algorithms (TALG) 14 (3), 1-31, 2018 | 47 | 2018 |
| The Franz-Parisi criterion and computational trade-offs in high dimensional statistics AS Bandeira, A El Alaoui, S Hopkins, T Schramm, AS Wein, I Zadik Advances in Neural Information Processing Systems 35, 33831-33844, 2022 | 33 | 2022 |
| Tight lower bounds for planted clique in the degree-4 SOS program P Raghavendra, T Schramm arXiv preprint arXiv:1507.05136, 2015 | 30 | 2015 |
| The Strongish Planted Clique Hypothesis and Its Applications P Manurangsi, A Rubinstein, T Schramm | 28* | 2021 |
| The threshold for SDP-refutation of random regular NAE-3SAT Y Deshpande, A Montanari, R O'Donnell, T Schramm, S Sen Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 26 | 2019 |
| A robust spectral algorithm for overcomplete tensor decomposition SB Hopkins, T Schramm, J Shi Conference on Learning Theory, 1683-1722, 2019 | 25 | 2019 |
| Testing thresholds for high-dimensional sparse random geometric graphs S Liu, S Mohanty, T Schramm, E Yang Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022 | 24 | 2022 |
| Playing unique games on certified small-set expanders M Bafna, B Barak, PK Kothari, T Schramm, D Steurer Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 24 | 2021 |
| Non-asymptotic approximations of neural networks by Gaussian processes R Eldan, D Mikulincer, T Schramm Conference on Learning Theory, 1754-1775, 2021 | 23 | 2021 |
| (Nearly) efficient algorithms for the graph matching problem on correlated random graphs B Barak, CN Chou, Z Lei, T Schramm, Y Sheng Advances in Neural Information Processing Systems 32, 2019 | 21 | 2019 |
