| 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 | 339 | 2017 |
| Distributed inference acceleration with adaptive DNN partitioning and offloading T Mohammed, C Joe-Wong, R Babbar, M Di Francesco IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 854-863, 2020 | 277 | 2020 |
| Data scarcity, robustness and extreme multi-label classification R Babbar, B Schölkopf Machine Learning 108 (8), 1329-1351, 2019 | 202 | 2019 |
| Bonsai: diverse and shallow trees for extreme multi-label classification S Khandagale, H Xiao, R Babbar Machine Learning 109 (11), 2099-2119, 2020 | 200 | 2020 |
| On flat versus hierarchical classification in large-scale taxonomies R Babbar, I Partalas, E Gaussier, MR Amini 27th Annual Conference on Neural Information Processing Systems (NIPS 26 …, 2013 | 99 | 2013 |
| Extreme classification (dagstuhl seminar 18291) S Bengio, K Dembczynski, T Joachims, M Kloft, M Varma Dagstuhl Reports 8 (7), 62-80, 2019 | 43 | 2019 |
| Learning taxonomy adaptation in large-scale classification R Babbar, I Partalas, E Gaussier, MR Amini, C Amblard Journal of Machine Learning Research 17 (98), 1-37, 2016 | 42 | 2016 |
| CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification S Kharbanda, A Banerjee, E Schultheis, R Babbar Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022 | 41 | 2022 |
| On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification E Schultheis, M Wydmuch, R Babbar, K Dembczynski Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 41 | 2022 |
| Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels M Qaraei, E Schultheis, P Gupta, R Babbar Proceedings of the Web Conference 2021, 3711-3720, 2021 | 40* | 2021 |
| 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 | 40 | 2010 |
| 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 | 28 | 2018 |
| 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 | 28 | 2014 |
| Inceptionxml: A lightweight framework with synchronized negative sampling for short text extreme classification S Kharbanda, A Banerjee, D Gupta, A Palrecha, R Babbar Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 26* | 2023 |
| Speeding-up one-versus-all training for extreme classification via mean-separating initialization E Schultheis, R Babbar Machine Learning 111 (11), 3953-3976, 2022 | 25* | 2022 |
| Adversarial extreme multi-label classification R Babbar, B Schölkopf arXiv preprint arXiv:1803.01570, 2018 | 23 | 2018 |
| Explainable publication year prediction of eighteenth century texts with the BERT model I Rastas, YC Ryan, ILI Tiihonen, M Qaraei, L Repo, R Babbar, E Mäkelä, ... Proceedings of the 3rd Workshop on Computational Approaches to Historical …, 2022 | 22 | 2022 |
| 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 | 16 | 2013 |
| Machine Learning under Resource Constraints-Fundamentals K Morik, P Marwedel Walter de Gruyter GmbH & Co KG, 2022 | 15 | 2022 |
| Adversarial examples for extreme multilabel text classification M Qaraei, R Babbar Machine Learning 111 (12), 4539-4563, 2022 | 14 | 2022 |
Erik SchultheisPostdoc, IST AustriaVerified email at aalto.fi
