Predicting clinical response to anticancer drugs using an *ex vivo* platform that captures tumour heterogeneityB Majumder, U Baraneedharan, S Thiyagarajan, P Radhakrishnan, ... Nature communications 6, 6169, 2015 | 128 | 2015 |

On the statistical consistency of plug-in classifiers for non-decomposable performance measures H Narasimhan, R Vaish, S Agarwal Advances in Neural Information Processing Systems, 1493-1501, 2014 | 54 | 2014 |

On the statistical consistency of algorithms for binary classification under class imbalance A Menon, H Narasimhan, S Agarwal, S Chawla International Conference on Machine Learning, 603-611, 2013 | 47 | 2013 |

A structural SVM based approach for optimizing partial AUC H Narasimhan, S Agarwal International Conference on Machine Learning, 516-524, 2013 | 46 | 2013 |

Online and stochastic gradient methods for non-decomposable loss functions P Kar, H Narasimhan, P Jain Advances in Neural Information Processing Systems, 694-702, 2014 | 36 | 2014 |

SVM pAUC tight: a new support vector method for optimizing partial AUC based on a tight convex upper bound H Narasimhan, S Agarwal Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 31 | 2013 |

Optimizing non-decomposable performance measures: A tale of two classes H Narasimhan, P Kar, P Jain International Conference on Machine Learning, 199-208, 2015 | 27 | 2015 |

Learnability of influence in networks H Narasimhan, DC Parkes, Y Singer Advances in Neural Information Processing Systems, 3186-3194, 2015 | 26 | 2015 |

Online optimization methods for the quantification problem P Kar, S Li, H Narasimhan, S Chawla, F Sebastiani Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 25 | 2016 |

Consistent multiclass algorithms for complex performance measures H Narasimhan, H Ramaswamy, A Saha, S Agarwal International Conference on Machine Learning, 2398-2407, 2015 | 25 | 2015 |

Surrogate functions for maximizing precision at the top P Kar, H Narasimhan, P Jain International Conference on Machine Learning, 189-198, 2015 | 19 | 2015 |

On the relationship between binary classification, bipartite ranking, and binary class probability estimation H Narasimhan, S Agarwal Advances in Neural Information Processing Systems, 2913-2921, 2013 | 19 | 2013 |

Optimal auctions through deep learning P Dütting, Z Feng, H Narasimhan, DC Parkes, SS Ravindranath arXiv preprint arXiv:1706.03459, 2017 | 18 | 2017 |

Learning with complex loss functions and constraints H Narasimhan International Conference on Artificial Intelligence and Statistics, 1646-1654, 2018 | 16 | 2018 |

Support vector algorithms for optimizing the partial area under the ROC curve H Narasimhan, S Agarwal Neural computation 29 (7), 1919-1963, 2017 | 12 | 2017 |

Deep Learning for Multi-Facility Location Mechanism Design. N Golowich, H Narasimhan, DC Parkes IJCAI, 261-267, 2018 | 10 | 2018 |

Automated mechanism design without money via machine learning H Narasimhan, SB Agarwal, DC Parkes Proceedings of the 25th International Joint Conference on Artificial …, 2016 | 9 | 2016 |

Deep learning for revenue-optimal auctions with budgets Z Feng, H Narasimhan, DC Parkes Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 6 | 2018 |

Optimizing the multiclass F-measure via biconcave programming H Narasimhan, W Pan, P Kar, P Protopapas, HG Ramaswamy 2016 IEEE 16th International Conference on Data Mining (ICDM), 1101-1106, 2016 | 5 | 2016 |

Stochastic optimization techniques for quantification performance measures H Narasimhan, S Li, P Kar, S Chawla, F Sebastiani stat 1050, 13, 2016 | 5 | 2016 |