| Learning to detect objects in images via a sparse, part-based representation S Agarwal, A Awan, D Roth IEEE transactions on pattern analysis and machine intelligence 26 (11), 1475 …, 2004 | 1215 | 2004 |
| Learning to detect objects in images via a sparse, part-based representation S Agarwal, A Awan, D Roth IEEE transactions on pattern analysis and machine intelligence 26 (11), 1475 …, 2004 | 1214 | 2004 |
| Learning a sparse representation for object detection S Agarwal, D Roth European conference on computer vision, 113-127, 2002 | 789 | 2002 |
| Generalization Bounds for the Area Under the ROC Curve. S Agarwal, T Graepel, R Herbrich, S Har-Peled, D Roth, MI Jordan Journal of Machine Learning Research 6 (4), 2005 | 288 | 2005 |
| 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 | 277 | 2015 |
| A statistical convergence perspective of algorithms for rank aggregation from pairwise data A Rajkumar, S Agarwal International Conference on Machine Learning, 118-126, 2014 | 172 | 2014 |
| Generalization bounds for ranking algorithms via algorithmic stability S Agarwal, P Niyogi Journal of Machine Learning Research 10 (Feb), 441-474, 2009 | 169 | 2009 |
| A differentially private stochastic gradient descent algorithm for multiparty classification A Rajkumar, S Agarwal Artificial Intelligence and Statistics, 933-941, 2012 | 147 | 2012 |
| Ranking on graph data S Agarwal Proceedings of the 23rd international conference on Machine learning, 25-32, 2006 | 144 | 2006 |
| Ranking chemical structures for drug discovery: a new machine learning approach S Agarwal, D Dugar, S Sengupta Journal of chemical information and modeling 50 (5), 716-731, 2010 | 138 | 2010 |
| 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 | 106 | 2013 |
| Stability and generalization of bipartite ranking algorithms S Agarwal, P Niyogi International Conference on Computational Learning Theory, 32-47, 2005 | 103 | 2005 |
| Learning with limited rounds of adaptivity: Coin tossing, multi-armed bandits, and ranking from pairwise comparisons A Agarwal, S Agarwal, S Assadi, S Khanna Conference on Learning Theory, 39-75, 2017 | 89 | 2017 |
| 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 | 89 | 2014 |
| The infinite push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list S Agarwal Proceedings of the 2011 SIAM International Conference on Data Mining, 839-850, 2011 | 83 | 2011 |
| A structural SVM based approach for optimizing partial AUC H Narasimhan, S Agarwal International Conference on Machine Learning, 516-524, 2013 | 82 | 2013 |
| Surrogate regret bounds for bipartite ranking via strongly proper losses S Agarwal The Journal of Machine Learning Research 15 (1), 1653-1674, 2014 | 69 | 2014 |
| Consistent multiclass algorithms for complex performance measures H Narasimhan, H Ramaswamy, A Saha, S Agarwal International Conference on Machine Learning, 2398-2407, 2015 | 57 | 2015 |
| 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 | 57 | 2013 |
| Learning algorithm for ranking on graph data S Agarwal US Patent 8,332,333, 2012 | 56 | 2012 |
