Rohit Babbar
Title
Cited by
Cited by
Year
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
1612017
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
722013
Data scarcity, robustness and extreme multi-label classification
R Babbar, B Schölkopf
Machine Learning 108 (8), 1329-1351, 2019
582019
Bonsai: diverse and shallow trees for extreme multi-label classification
S Khandagale, H Xiao, R Babbar
Machine Learning 109 (11), 2099-2119, 2020
482020
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
332010
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
242014
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
232016
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
152013
Extreme classification (dagstuhl seminar 18291)
S Bengio, K Dembczynski, T Joachims, M Kloft, M Varma
Dagstuhl Reports 8 (7), 2019
142019
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
132020
Adversarial extreme multi-label classification
R Babbar, B Schölkopf
arXiv preprint arXiv:1803.01570, 2018
122018
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
92018
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
82016
Re-ranking approach to classification in large-scale power-law distributed category systems
R Babbar, I Partalas, E Gaussier, MR Amini
Proceedings of the 37th international ACM SIGIR conference on Research …, 2014
82014
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
42015
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
42012
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
32013
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
32012
Unbiased Loss Functions for Extreme Classification With Missing Labels
E Schultheis, M Qaraei, P Gupta, R Babbar
ICML 2020 workshop on Extreme Classification, 2020
22020
Machine Learning Strategies for Large-scale Taxonomies
R Babbar
Université de Grenoble, 2014
22014
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Articles 1–20