| Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization HG Ramaswamy proceedings of the IEEE/CVF winter conference on applications of computer …, 2020 | 372 | 2020 |
| Mixture proportion estimation via kernel embeddings of distributions H Ramaswamy, C Scott, A Tewari International conference on machine learning, 2052-2060, 2016 | 203 | 2016 |
| Consistent algorithms for multiclass classification with an abstain option HG Ramaswamy, A Tewari, S Agarwal | 74 | 2018 |
| Consistent multiclass algorithms for complex performance measures H Narasimhan, H Ramaswamy, A Saha, S Agarwal International Conference on Machine Learning, 2398-2407, 2015 | 69 | 2015 |
| On controllable sparse alternatives to softmax A Laha, SA Chemmengath, P Agrawal, M Khapra, K Sankaranarayanan, ... Advances in neural information processing systems 31, 2018 | 56 | 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 | 50 | 2016 |
| Convex calibration dimension for multiclass loss matrices HG Ramaswamy, S Agarwal Journal of Machine Learning Research 17 (14), 1-45, 2016 | 50 | 2016 |
| Convex calibrated surrogates for low-rank loss matrices with applications to subset ranking losses HG Ramaswamy, S Agarwal, A Tewari Advances in Neural Information Processing Systems 26, 2013 | 37 | 2013 |
| Classification calibration dimension for general multiclass losses HG Ramaswamy, S Agarwal Advances in Neural Information Processing Systems 25, 2012 | 32 | 2012 |
| Consistent algorithms for multiclass classification with a reject option HG Ramaswamy, A Tewari, S Agarwal arXiv preprint arXiv:1505.04137, 2015 | 23 | 2015 |
| On knowledge distillation from complex networks for response prediction S Arora, MM Khapra, HG Ramaswamy Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 22 | 2019 |
| Convex calibrated surrogates for hierarchical classification H Ramaswamy, A Tewari, S Agarwal International Conference on Machine Learning, 1852-1860, 2015 | 22 | 2015 |
| On the consistency of output code based learning algorithms for multiclass learning problems HG Ramaswamy, BS Babu, S Agarwal, RC Williamson Conference on Learning Theory, 885-902, 2014 | 18 | 2014 |
| Convex calibrated surrogates for the multi-label f-measure M Zhang, HG Ramaswamy, S Agarwal International Conference on Machine Learning, 11246-11255, 2020 | 17 | 2020 |
| Cognitive commuter assistant SRK Penubothula, LK Namboodiri, PK Jayachandran, HG Ramaswamy, ... US Patent 11,423,280, 2022 | 13 | 2022 |
| Consistent plug-in classifiers for complex objectives and constraints SK Tavker, HG Ramaswamy, H Narasimhan Advances in Neural Information Processing Systems 33, 20366-20377, 2020 | 7 | 2020 |
| Consistent multiclass algorithms for complex metrics and constraints H Narasimhan, HG Ramaswamy, SK Tavker, D Khurana, P Netrapalli, ... arXiv preprint arXiv:2210.09695, 2022 | 4 | 2022 |
| On the Interpretability of Attention Networks LN Pandey, R Vashisht, HG Ramaswamy Asian Conference on Machine Learning, 832-847, 2023 | 3 | 2023 |
| Inductive bias of gradient descent for weight normalized smooth homogeneous neural nets D Morwani, HG Ramaswamy International Conference on Algorithmic Learning Theory, 827-880, 2022 | 3 | 2022 |
| Using noise resilience for ranking generalization of deep neural networks D Morwani, R Vashisht, HG Ramaswamy arXiv preprint arXiv:2012.08854, 2020 | 2 | 2020 |
