| 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), 6169, 2015 | 318 | 2015 |
| Optimal auctions through deep learning P Dütting, Z Feng, H Narasimhan, D Parkes, SS Ravindranath International Conference on Machine Learning, 1706-1715, 2019 | 207 | 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 | 132 | 2013 |
| Robust optimization for fairness with noisy protected groups S Wang, W Guo, H Narasimhan, A Cotter, M Gupta, M Jordan Advances in neural information processing systems 33, 5190-5203, 2020 | 119 | 2020 |
| 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 | 113 | 2020 |
| Learning with complex loss functions and constraints H Narasimhan International Conference on Artificial Intelligence and Statistics, 1646-1654, 2018 | 104 | 2018 |
| 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 27, 2014 | 95 | 2014 |
| 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 | 89 | 2018 |
| A structural SVM based approach for optimizing partial AUC H Narasimhan, S Agarwal International Conference on Machine Learning, 516-524, 2013 | 86 | 2013 |
| Deep Learning for Multi-Facility Location Mechanism Design. N Golowich, H Narasimhan, DC Parkes IJCAI, 261-267, 2018 | 82 | 2018 |
| Online and stochastic gradient methods for non-decomposable loss functions P Kar, H Narasimhan, P Jain Advances in Neural Information Processing Systems 27, 2014 | 77 | 2014 |
| Learnability of influence in networks H Narasimhan, DC Parkes, Y Singer Advances in Neural Information Processing Systems 28, 2015 | 76 | 2015 |
| Optimal auctions through deep learning P Dütting, Z Feng, H Narasimhan, DC Parkes, SS Ravindranath Communications of the ACM 64 (8), 109-116, 2021 | 72 | 2021 |
| Consistent multiclass algorithms for complex performance measures H Narasimhan, H Ramaswamy, A Saha, S Agarwal International Conference on Machine Learning, 2398-2407, 2015 | 70 | 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 | 63 | 2016 |
| SVMpAUCtight 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 | 61 | 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 | 59 | 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 | 51 | 2016 |
| Surrogate functions for maximizing precision at the top P Kar, H Narasimhan, P Jain International Conference on Machine Learning, 189-198, 2015 | 48 | 2015 |
| On the relationship between binary classification, bipartite ranking, and binary class probability estimation H Narasimhan, S Agarwal Advances in neural information processing systems 26, 2013 | 45 | 2013 |
