| DiSMEC : Distributed Sparse Machines for Extreme Multi-label Classification R Babbar, B Schölkopf Proceedings of the Tenth ACM International Conference on Web Search and Data …, 2017 | 135 | 2017 |
| On flat versus hierarchical classification in large-scale taxonomies R Babbar, I Partalas, E Gaussier, MR Amini Advances in neural information processing systems 26, 1824-1832, 2013 | 65 | 2013 |
| Data scarcity, robustness and extreme multi-label classification R Babbar, B Schölkopf Machine Learning 108 (8-9), 1329-1351, 2019 | 35 | 2019 |
| Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text R Babbar, N Singh Proceedings of the fourth workshop on Analytics for noisy unstructured text …, 2010 | 29 | 2010 |
| Bonsai--Diverse and Shallow Trees for Extreme Multi-label Classification S Khandagale, H Xiao, R Babbar arXiv preprint arXiv:1904.08249, 2019 | 23 | 2019 |
| On power law distributions in large-scale taxonomies R Babbar, C Metzig, I Partalas, E Gaussier, MR Amini ACM SIGKDD Explorations Newsletter 16 (1), 47-56, 2014 | 23 | 2014 |
| Learning taxonomy adaptation in large-scale classification R Babbar, I Partalas, E Gaussier, MR Amini, C Amblard The Journal of Machine Learning Research 17 (1), 3350-3386, 2016 | 22 | 2016 |
| Maximum-margin framework for training data synchronization in large-scale hierarchical classification R Babbar, I Partalas, E Gaussier, MR Amini International Conference on Neural Information Processing, 336-343, 2013 | 14 | 2013 |
| Adversarial extreme multi-label classification R Babbar, B Schölkopf arXiv preprint arXiv:1803.01570, 2018 | 11 | 2018 |
| Re-ranking approach to classification in large-scale power-law distributed category systems R Babbar, I Partalas, E Gaussier, M Amini Proceedings of the 37th international ACM SIGIR conference on Research …, 2014 | 9 | 2014 |
| TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification R Babbar, K Muandet, B Schölkopf SIAM International Conference on Data Mining, 234-242, 2016 | 8 | 2016 |
| Extreme classification (dagstuhl seminar 18291) S Bengio, K Dembczynski, T Joachims, M Kloft, M Varma Dagstuhl Reports 8 (7), 2019 | 7 | 2019 |
| Prediction of glucose tolerance without an oral glucose tolerance test R Babbar, M Heni, A Peter, M Hrabě de Angelis, HU Häring, A Fritsche, ... Frontiers in endocrinology 9, 82, 2018 | 7 | 2018 |
| Efficient model selection for regularized classification by exploiting unlabeled data G Balikas, I Partalas, E Gaussier, R Babbar, MR Amini International Symposium on Intelligent Data Analysis, 25-36, 2015 | 4 | 2015 |
| Adaptive classifier selection in large-scale hierarchical classification I Partalas, R Babbar, E Gaussier, C Amblard International Conference on Neural Information Processing, 612-619, 2012 | 4 | 2012 |
| Bonsai: diverse and shallow trees for extreme multi-label classification S Khandagale, H Xiao, R Babbar Machine Learning 109 (11), 2099-2119, 2020 | 3 | 2020 |
| Comparative classifier evaluation for web-scale taxonomies using power law R Babbar, I Partalas, C Metzig, E Gaussier, M Amini Extended Semantic Web Conference, 310-311, 2013 | 3 | 2013 |
| On empirical tradeoffs in large scale hierarchical classification R Babbar, I Partalas, E Gaussier, C Amblard Proceedings of the 21st ACM international conference on Information and …, 2012 | 3 | 2012 |
| Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading T Mohammed, C Joe-Wong, R Babbar, M Di Francesco | 3 | |
| Machine Learning Strategies for Large-scale Taxonomies R Babbar | 2 | 2014 |
Ioannis PartalasMachine Learning ScientistVerified email at expedia.com
