Detecting high log-densities: an *O*(*n*^{¼}) approximation for densest *k*-subgraphA Bhaskara, M Charikar, E Chlamtac, U Feige, A Vijayaraghavan Proceedings of the forty-second ACM symposium on Theory of computing, 201-210, 2010 | 302 | 2010 |

Smoothed analysis of tensor decompositions A Bhaskara, M Charikar, A Moitra, A Vijayaraghavan Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 116 | 2014 |

Polynomial integrality gaps for strong SDP relaxations of Densest *k*-subgraphA Bhaskara, M Charikar, V Guruswami, A Vijayaraghavan, Y Zhou Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete …, 2012 | 112 | 2012 |

Learning mixtures of ranking models P Awasthi, A Blum, O Sheffet, A Vijayaraghavan arXiv preprint arXiv:1410.8750, 2014 | 62 | 2014 |

Approximation algorithms for semi-random partitioning problems K Makarychev, Y Makarychev, A Vijayaraghavan Proceedings of the forty-fourth annual ACM symposium on Theory of computing …, 2012 | 61 | 2012 |

Bilu–Linial stable instances of max cut and minimum multiway cut K Makarychev, Y Makarychev, A Vijayaraghavan Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014 | 55 | 2014 |

Uniqueness of tensor decompositions with applications to polynomial identifiability A Bhaskara, M Charikar, A Vijayaraghavan Conference on Learning Theory (COLT) 2014 35, 742–778, 2014 | 50 | 2014 |

Approximating Matrix *p*-normsA Bhaskara, A Vijayaraghavan Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete …, 2011 | 41 | 2011 |

On learning mixtures of well-separated gaussians O Regev, A Vijayaraghavan 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), 85-96, 2017 | 40 | 2017 |

Approximation Algorithms and Hardness of the *k*-Route Cut ProblemJ Chuzhoy, Y Makarychev, A Vijayaraghavan, Y Zhou ACM Transactions on Algorithms (TALG) 12 (1), 1-40, 2015 | 33 | 2015 |

Beating the random assignment on constraint satisfaction problems of bounded degree B Barak, A Moitra, R O'Donnell, P Raghavendra, O Regev, D Steurer, ... arXiv preprint arXiv:1505.03424, 2015 | 32 | 2015 |

Correlation clustering with noisy partial information K Makarychev, Y Makarychev, A Vijayaraghavan Conference on Learning Theory, 1321-1342, 2015 | 29 | 2015 |

Learning communities in the presence of errors K Makarychev, Y Makarychev, A Vijayaraghavan Conference on Learning Theory, 1258-1291, 2016 | 24 | 2016 |

Constant factor approximation for balanced cut in the PIE model K Makarychev, Y Makarychev, A Vijayaraghavan Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 24 | 2014 |

Approximation algorithms for label cover and the log-density threshold E Chlamtáč, P Manurangsi, D Moshkovitz, A Vijayaraghavan Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 18 | 2017 |

Sorting noisy data with partial information K Makarychev, Y Makarychev, A Vijayaraghavan Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013 | 18 | 2013 |

On robustness to adversarial examples and polynomial optimization P Awasthi, A Dutta, A Vijayaraghavan arXiv preprint arXiv:1911.04681, 2019 | 14 | 2019 |

Clustering stable instances of euclidean k-means A Vijayaraghavan, A Dutta, A Wang Proceedings of the Neural Information Processing Systems (NIPS), 2017 | 13 | 2017 |

Clustering stable instances of euclidean k-means A Dutta, A Vijayaraghavan, A Wang arXiv preprint arXiv:1712.01241, 2017 | 9 | 2017 |

Smoothed analysis in unsupervised learning via decoupling A Bhaskara, A Chen, A Perreault, A Vijayaraghavan 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS …, 2019 | 6 | 2019 |