Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity B Majumder, U Baraneedharan, S Thiyagarajan, P Radhakrishnan, ... Nature communications 6 (1), 1-14, 2015 | 221 | 2015 |

Optimal auctions through deep learning P Dütting, Z Feng, H Narasimhan, D Parkes, SS Ravindranath International Conference on Machine Learning, 1706-1715, 2019 | 118 | 2019 |

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 | 78 | 2013 |

On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures. H Narasimhan, R Vaish, S Agarwal NIPS 27, 1493-1501, 2014 | 77 | 2014 |

A structural SVM based approach for optimizing partial AUC H Narasimhan, S Agarwal International Conference on Machine Learning, 516-524, 2013 | 64 | 2013 |

Online and stochastic gradient methods for non-decomposable loss functions P Kar, H Narasimhan, P Jain arXiv preprint arXiv:1410.6776, 2014 | 60 | 2014 |

Learning with complex loss functions and constraints H Narasimhan International Conference on Artificial Intelligence and Statistics, 1646-1654, 2018 | 56 | 2018 |

Learnability of influence in networks H Narasimhan, DC Parkes, Y Singer Proceedings of the 29th Annual Conference on Neural Information Processing …, 2015 | 55 | 2015 |

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 | 49 | 2018 |

Consistent multiclass algorithms for complex performance measures H Narasimhan, H Ramaswamy, A Saha, S Agarwal International Conference on Machine Learning, 2398-2407, 2015 | 46 | 2015 |

SVM_{pAUC}^{tight} a new support vector method for optimizing partial AUC based on a tight convex upper boundH Narasimhan, S Agarwal Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 46 | 2013 |

Deep Learning for Multi-Facility Location Mechanism Design. N Golowich, H Narasimhan, DC Parkes IJCAI, 261-267, 2018 | 45 | 2018 |

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 | 44 | 2016 |

Optimizing non-decomposable performance measures: A tale of two classes H Narasimhan, P Kar, P Jain International Conference on Machine Learning, 199-208, 2015 | 43 | 2015 |

On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation. H Narasimhan, S Agarwal NIPS, 2913-2921, 2013 | 38 | 2013 |

Pairwise fairness for ranking and regression H Narasimhan, A Cotter, M Gupta, S Wang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5248-5255, 2020 | 37 | 2020 |

Surrogate functions for maximizing precision at the top P Kar, H Narasimhan, P Jain International Conference on Machine Learning, 189-198, 2015 | 32 | 2015 |

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 | 26 | 2016 |

Automated mechanism design without money via machine learning H Narasimhan, SB Agarwal, DC Parkes Proceedings of the 25th International Joint Conference on Artificial …, 2016 | 26 | 2016 |

Support vector algorithms for optimizing the partial area under the ROC curve H Narasimhan, S Agarwal Neural computation 29 (7), 1919-1963, 2017 | 25 | 2017 |